question stringclasses 91 values | options listlengths 2 4 | answer stringlengths 1 182 | question_type stringclasses 3 values | ts1 listlengths 1.02k 1.08k ⌀ | ts2 listlengths 1.02k 1.2k ⌀ | tid int64 1 102 | difficulty stringclasses 3 values | format_hint stringclasses 1 value | relevant_concepts listlengths 1 8 | question_hint stringclasses 90 values | category stringclasses 5 values | subcategory stringclasses 13 values | id int64 1 746 | ts listlengths 1.02k 1.2k ⌀ |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Is the given time series likely to have an anomaly? | [
"No",
"Yes, it's pattern is flipped at certain point in time",
"Yes, it's pattern is distorted by random spikes or noises"
] | No | binary | null | null | 63 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Flip Anomaly",
"Spike Anomaly"
] | Anomaly is an observation that deviates from the general pattern in the time series. You should check if the time series has any sudden changes or unexpected patterns. If so, check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 201 | [
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2.3987834703737336,
2.1308972597901756,
1.808170... |
The given time series is a sawtooth wave. What is the most likely amplitude of the sawtooth wave? | [
"1.08",
"5.47",
"19.62"
] | 1.08 | multiple-choice | null | null | 23 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sawtooth Wave",
"Amplitude"
] | Check the distance between the peak and the baseline. | Pattern Recognition | Cycle Recognition | 202 | [
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-0.676352597402073,
-0.49552481146029004,
-0.7101455137657977,
-0.6841980012550006,... |
The time series has a trend and cyclic component added together. Which components are most likely present in the given time series? | [
"Exponential trend and sine wave",
"No trend and sawtooth wave",
"Linear trend and sine wave"
] | Exponential trend and sine wave | multiple-choice | null | null | 26 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend",
"Sine Wave",
"Sawtooth Wave",
"Additive Composition"
] | For trend, check if the slope is constant or changes over time. For cyclic component, check the overall shape of the time series. | Pattern Recognition | Cycle Recognition | 203 | [
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3.2896762932508095,
-1.395675285... |
You are given two time series following similar pattern. One has an anomaly and the other does not. Which time series has the anomaly, and what is the likely type of anomaly? | [
"Time series 1 with speed up/down anomaly: the period of cyclic components is different from other parts of the time series",
"Time series 2 with cutoff anomaly: the pattern of time series disappeared for certain point in time and became flat",
"Time series 1 with flip anomaly: the pattern is flipped at certain... | Time series 2 with cutoff anomaly: the pattern of time series disappeared for certain point in time and became flat | multiple_choice | [
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... | [
0,
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1.74396... | 73 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Linear Trend",
"Speed Up/Down Anomaly",
"Cutoff Anomaly",
"Flip Anomaly"
] | You should first identify the time series with the anomaly. Remember, both time series share similar pattern. Then, you should check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 204 | null |
What type of noise is present in the given time series? | [
"Red Noise",
"No significant noise",
"Gaussian White Noise"
] | Gaussian White Noise | multiple_choice | null | null | 62 | medium | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Gaussian White Noise",
"Red Noise"
] | Observe the pattern of fluctuations in the time series. | Noise Understanding | Signal to Noise Ratio Understanding | 205 | [
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-0.7631685494713448,
-0.003765831... |
The time series has a trend and cyclic component added together. Which components are most likely present in the given time series? | [
"No trend and sawtooth wave",
"Linear trend and sine wave",
"Exponential trend and sine wave"
] | Linear trend and sine wave | multiple-choice | null | null | 26 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend",
"Sine Wave",
"Sawtooth Wave",
"Additive Composition"
] | For trend, check if the slope is constant or changes over time. For cyclic component, check the overall shape of the time series. | Pattern Recognition | Cycle Recognition | 206 | [
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0.51... |
The given time series is a sine wave. What is the most likely amplitude of the sine wave? | [
"18.66",
"8.44",
"1.18"
] | 8.44 | multiple-choice | null | null | 21 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Amplitude"
] | Check the distance between the peak and the baseline. | Pattern Recognition | Cycle Recognition | 207 | [
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7.994538167553252,
... |
You are given two time series with same underlying pattern but different noise level (variance). Which time series has higher magnitude of noise? | [
"Time series 2",
"Time series 1"
] | Time series 1 | multiple_choice | [
1.3837906282959942,
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6.8912500455580705,
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3.6139521091339... | [
0.9108367215457523,
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4.003380095190174,
4.082014313782182,
4.0261376348855835,
4.106702979083305,
3.90773094385828... | 60 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Exponential Trend",
"Gaussian White Noise",
"Variance"
] | When the noise level is high, it can distort the pattern in the time series. Both time series have the same underlying pattern, but different noise level. To tell which time series has higher noise level, you should check the degree of distortion of the time series pattern. | Noise Understanding | Signal to Noise Ratio Understanding | 208 | null |
Does time series 1 granger cause time series 2? | [
"No, time series 2 granger causes time series 1",
"Yes, time series 1 granger causes time series 2",
"No, they are not granger causal"
] | Yes, time series 1 granger causes time series 2 | binary | [
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... | [
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-1.2837105922966239,
-0.4773292384830545... | 101 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Granger Causality"
] | Granger causality is a statistical concept that determines whether one time series can predict another. While you cannot perform the statistical test, you can check if one time series can predict the other by shifting the time series by a certain number of steps. Do they look simiar after the shift? | Causality Analysis | Granger Causality | 209 | null |
The following time series has a noise component, a trend component, and a cyclic component. Is the noise component more likely to be a white noise or random walk? | [
"White Noise",
"Random Walk"
] | Random Walk | binary | null | null | 52 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Red Noise",
"Gaussian White Noise"
] | White noise is a stationary process with a constant mean and variance. You should check if the time series has a constant mean and variance over time. This can help you distinguish between white noise and random walk. | Noise Understanding | White Noise Recognition | 210 | [
-0.020358514678560143,
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0.911747711495775,
1.1064928068523827,
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-0.18922942285865085,
-0.4731245756211171,
-... |
The given time series is a gaussian white noise process. What is the most likely noise level (variance)? | [
"9.48",
"5.21",
"1.24"
] | 5.21 | multiple_choice | null | null | 51 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Gaussian White Noise"
] | The noise level refers to the standard deviation of the noise. You should check the degree of variation of the time series over time. You can estimate the standard deviation by observing the average distance between the data points and the mean. | Noise Understanding | White Noise Recognition | 211 | [
-0.07477932773531216,
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-1.6000219662634223,
... |
Is the given time series a white noise process? | [
"No",
"Yes"
] | No | binary | null | null | 50 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Gaussian White Noise"
] | White noise is a stationary process with a constant mean and variance. You should check if the time series has a constant mean and variance over time. Another important property is that the noise is uncorrelated over time. Does the time series seem to have these properties? | Noise Understanding | White Noise Recognition | 212 | [
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5.584021235917692,
5.584021235917692,
5.584021235917692,
5.584021235917692,
5.584021235917692,
5.584021235917692,
5.584021235917692,
5.584021235917692,
5.584021235917692,
5.584021235917692,
5.584021235917692,
5.584021235917692,
5.584021235917692,
5... |
The given time series has a trend and a cyclic component. It also has an anomaly. What is the most likely combination of components without the anomaly? | [
"Linear trend and sine wave",
"Exponential trend and square wave",
"Log trend and sawtooth wave"
] | Exponential trend and square wave | multiple_choice | null | null | 70 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Sine Wave",
"Exponential Trend",
"Square Wave",
"Log Trend",
"Sawtooth Wave",
"Cutoff Anomaly",
"Flip Anomaly"
] | The anomaly only influences a small part of the time series. You should focus on the overall pattern of the time series without the anomaly. Can you recover the original pattern? | Anolmaly Detection | General Anomaly Detection | 213 | [
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... |
Is the given time series a white noise process? | [
"No",
"Yes"
] | No | binary | null | null | 50 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Gaussian White Noise"
] | White noise is a stationary process with a constant mean and variance. You should check if the time series has a constant mean and variance over time. Another important property is that the noise is uncorrelated over time. Does the time series seem to have these properties? | Noise Understanding | White Noise Recognition | 214 | [
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4.002551... |
You are given two time series following similar pattern. Both of them have an anomaly. Do they have the same type of anomaly? | [
"Yes, Time series 1 and time series 2 both have cutoff anomaly",
"No. They have different types of anomalies: cutoff and spikes"
] | Yes, Time series 1 and time series 2 both have cutoff anomaly | binary | [
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0,
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-1... | 75 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Cutoff Anomaly",
"Spike Anomaly"
] | For each time series, identify the type of anomaly based on the given definitions. Then, check if they have the same type of anomaly. | Anolmaly Detection | General Anomaly Detection | 215 | null |
Despite the noise, does the given two time series have similar pattern? | [
"No, they have different seasonal pattern",
"Yes, they have similar seasonal pattern"
] | Yes, they have similar seasonal pattern | binary | [
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0.2607904... | [
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2.1508261... | 79 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave"
] | Noise refers to the random fluctuations in the time series. You should focus on the overall pattern of the time series. Pattern refers to the general shape of the time series. In this case, you see both time series have cyclic patterns. Do their behaviors at peak and trough look similar? | Similarity Analysis | Shape | 216 | null |
One type of noise in time series is white noise. Is the given time series noisy (noise dominates other patterns) based on your understanding of white noise? | [
"Yes",
"No"
] | Yes | binary | null | null | 55 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Gaussian White Noise"
] | When we say a time series is noisy, it typically refers to there are random fluctuations that disrupt the overal pattern of the time series. Can you check if it is the case for the given time series? | Noise Understanding | Signal to Noise Ratio Understanding | 217 | [
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-... |
Does the given time series exhibit any monotonic increasing trend? | [
"Yes",
"No"
] | Yes | binary | null | null | 3 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend",
"Log Trend"
] | Check if the time series values increase over time. | Pattern Recognition | Trend Recognition | 218 | [
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1.4488... |
Two time series are given with different cyclic components. Which time series has a higher period of the cyclic component? | [
"Time series 2 has higher period",
"Time series 1 has higher period"
] | Time series 2 has higher period | binary | [
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2.56621... | [
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1.3640585324664356,
1.2689909153994499,
1.280608993023557,
1.3574314475561435,
1.375566265681528,
1.3740866004740577,
1.315977... | 84 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Period"
] | Period refers to the length of one cycle in the cyclic component. You should check the distance between two peaks or two troughs for both time series. | Similarity Analysis | Shape | 219 | null |
How does the noise in the given time series influence the detection of periodic pattern in the time series? | [
"Distort the pattern",
"No influence, Sinewave"
] | No influence, Sinewave | binary | null | null | 58 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Gaussian White Noise",
"Sine Wave",
"Additive Composition"
] | When the noise level is high, it can distort the pattern in the time series. Can you check if you can still detect the cyclic pattern in the time series? | Noise Understanding | Signal to Noise Ratio Understanding | 220 | [
-0.0956981786267344,
0.4996543504784677,
0.7679520473603791,
1.512730686740936,
1.941904309803216,
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2.183255957674452,
2.616253582555006,
2.468151188990143,
2.4108767929032107,
2.026738361283523,
2.0998912988654013,
1.4718803664582278,
1.0222790960143524,
0.53078674175... |
Piece-wise stationarity means a time series is stationary in distinct segments, with abrupt changes between segments. Each segment has its own constant statistical properties. Does the time series exhibit piecewise stationarity? | [
"No",
"Yes"
] | No | binary | null | null | 38 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity",
"Linear Trend",
"Gaussian White Noise"
] | Look for segments of the time series that are individually stationary, even if the whole series is not. | Pattern Recognition | Stationarity Detection | 221 | [
-0.028285367944270074,
0.06508784910843045,
0.016828036337792885,
-0.058122195107188544,
0.031843268807160346,
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0.12263885966113096,
-0.0059386865377254044,
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0.14298953603872705,
-0.03892487576309385,
0.2593831595352871,
0.02473786119095288,
-0.0075... |
The given time series is a swatooth wave followed by a square wave. What is the most likely period of the swatooth wave? | [
"59.93",
"15.69",
"31.09"
] | 31.09 | multiple-choice | null | null | 25 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sawtooth Wave",
"Square Wave",
"Period"
] | The sawtooth wave comes before the square wave. Begin by identifying where the sawtooth wave starts. Next, measure the time interval between two peaks. | Pattern Recognition | Cycle Recognition | 222 | [
-1.7980494873512556,
-1.376920120612236,
-1.258530697751288,
-1.0318931811462475,
-1.0570748987346765,
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-0.9072246120586627,
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-0.5798019977052066,
-0.7321273835738381,
-0.5787200851849175,
-0.5813664146153572,
-0.36151809000390706,
-0.371507337949790... |
Is the given time series likely to be stationary after removing the cycle component? | [
"No",
"Yes"
] | No | binary | null | null | 35 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity",
"Sine Wave",
"Square Wave"
] | Cycle component brings the cyclic pattern to the time series. Assume this effect is removed, does the time series satisfy the stationarity condition? | Pattern Recognition | Stationarity Detection | 223 | [
-0.11156523144081579,
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2.2284010001522265,
2.2524353895183467,
1.9970127348803886,
2.0422774112496196,
2.11634577... |
Two time series are given, one with an upward trend and the other with a downward trend. Do they exhibit similar patterns aside from the trend? | [
"Yes, they share a similar pattern",
"No, they have different cyclic components"
] | Yes, they share a similar pattern | binary | [
-0.05018179019790812,
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0.2518926573... | [
-0.09070460324442373,
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1.3978139907242164,
1.3463906721194177,
1.6183800754895357,
1.5771608594485214,
1.2706... | 89 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Sine Wave",
"Square Wave"
] | You should focus on the cyclic components of the time series. Do they have similar patterns aside from the trend? | Similarity Analysis | Shape | 224 | null |
Is the given time series strictly stationary? | [
"Yes",
"No"
] | Yes | binary | null | null | 30 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity"
] | Try to see if the time series has a constant mean, and degree of variation over time. | Pattern Recognition | Stationarity Detection | 225 | [
-0.0009900017047804188,
-0.08701835432547785,
0.08809792111630335,
-0.24705992293708373,
2.1533797020076837,
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1.3857452840606108,
-0.414128180428561... |
Is the given time series strictly stationary? | [
"Yes",
"No"
] | Yes | binary | null | null | 30 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity"
] | Try to see if the time series has a constant mean, and degree of variation over time. | Pattern Recognition | Stationarity Detection | 226 | [
0.6986012507854011,
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0.90579247531243,
0.915121540319595,
-1.5421392913672507,
-0.0632553585106176,
1.9248326886927303,
1.4337059138234942,
1.3159028057064008,
0.06751297077101806,
3.5388474886738748,
2.7338219455496313,
0.80576... |
Which of the following best describe the cycle pattern in the given time series? | [
"Period increase over time",
"Period decrease over time",
"Period remain the same over time"
] | Period increase over time | multiple-choice | null | null | 29 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Period"
] | Check the time interval between two peaks, and see how it changes over time. | Pattern Recognition | Cycle Recognition | 227 | [
-0.028722484763007084,
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0.45758646054510144,
0.8205319613992114,
0.8884132404104241,
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1.296119052190809,
1.4338300733429605,
1.3277698697664584,
1.324891686619483,
1.3787696690549565,
1.2557287269761939,
1.0697324426309804,
0.7919939650923199,
0.506... |
Is the two time series lagged version of each other despite minor noise? | [
"Yes, they are lagged versions",
"No, they are not lagged versions at all"
] | Yes, they are lagged versions | binary | [
0.172537515519026,
0.08389224373868674,
-0.08825242608853813,
-0.003778653196356184,
-0.03663096884173579,
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0.096869... | [
-0.054012213921548397,
-0.06075768116626931,
0.24871274621583572,
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-0.13065199930658297,
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0.010664043696159682,
-0.17110335224942225,
-0.11886924695052907,
0.07737064902658951,
0.144134... | 100 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Lagged Pair",
"Red Noise"
] | Try to shift one time series by a certain number of steps and check if it looks the same as the other time series despite the noise. If they are lagged versions, they should look very similar in general after the shift. | Causality Analysis | Granger Causality | 228 | null |
Which additive combination of patterns best describes the time series? | [
"SawtoothWave + SquareWave",
"SineWave + SquareWave",
"SineWave + SawtoothWave"
] | SawtoothWave + SquareWave | multiple-choice | null | null | 16 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Sawtooth Wave",
"Additive Composition"
] | Imagine the shape of the time series as addition of two different patterns. | Pattern Recognition | Cycle Recognition | 229 | [
-0.8146460350793564,
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0.5250236308444459,
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0.5684714193820115,
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0.6119192079195772,
0.6336431021883601,
0.6553669964571429,
0.6770908907259258,
0.6988147849947086,
0.7205386792634915,
0.7422625735322743,
0.7639864678010571,
0.78571... |
Are the given two time series likely to have the same underlying distribution? | [
"Yes, they have the same underlying distribution: AR(1)",
"No, they have different underlying distribution: AR(1) and MA(5)"
] | Yes, they have the same underlying distribution: AR(1) | binary | [
21.666240156286044,
12.346228267094187,
20.551521918858022,
0.32201858726637766,
6.759900359638938,
4.454692807335411,
22.040402965552733,
27.173134482802944,
16.307091325173715,
11.499490131724272,
-4.5179134829306395,
-3.9609569888272103,
-14.383142296614793,
-25.70532510473202,
-22.78... | [
-2.551011649106549,
-8.48465984791953,
1.3941410461834618,
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26.892288821180905,
34.87317004007539,
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21.180845957236063,
19.84303976090134,
14.692078977086327,
4.463680785069343,
-1.54292188285... | 92 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"AutoRegressive Process",
"Moving Average Process"
] | The difference between AR(1) and MA(1) is that AR(1) is a linear combination of past values and white noise, while MA(1) is a linear combination of past white noise values. You should check if the time series exhibit any dependency on the previous values. This could give you a clue about whether the time series is AR(1) or not. Check this for both time series. | Similarity Analysis | Distributional | 230 | null |
You are given two time series following similar pattern. One has an anomaly and the other does not. Which time series has the anomaly, and what is the likely type of anomaly? | [
"Time series 1 with speed up/down anomaly: the period of cyclic components is different from other parts of the time series",
"Time series 2 with cutoff anomaly: the pattern of time series disappeared for certain point in time and became flat",
"Time series 1 with flip anomaly: the pattern is flipped at certain... | Time series 1 with speed up/down anomaly: the period of cyclic components is different from other parts of the time series | multiple_choice | [
0,
0.3217924151215423,
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1.0823053443333297,
1.2110669348377934,
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1.222374013987892,
1.1030766680837276,
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0.3439051349024977,
0.009725236677975022,
-0.33176510463628595,
-0.6591084583294577,
... | [
0,
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1.780267676905957,
1.803257777716424,
1.7969636715597783,
1.7615667014040115,
1.697735... | 73 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Linear Trend",
"Speed Up/Down Anomaly",
"Cutoff Anomaly",
"Flip Anomaly"
] | You should first identify the time series with the anomaly. Remember, both time series share similar pattern. Then, you should check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 231 | null |
Does the given two time series have similar pattern? | [
"Yes, they have similar seasonal pattern",
"No, they have different seasonable pattern"
] | No, they have different seasonable pattern | binary | [
0,
0.30835317097651244,
0.6106513091377529,
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1.900021... | [
0,
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7.952039489631392,
7.952039489631392,
7.952039489631392,
7.952039489631392,
7.952039489631392... | 78 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave"
] | Pattern refers to the general shape of the time series. In this case, you see both time series have cyclic patterns. Do their behaviors at peak and trough look similar? | Similarity Analysis | Shape | 232 | null |
You are seeing two instances of random walk. Are they likely to have the same variance? | [
"Yes, they have the same variance",
"No, time series 2 has higher variance",
"No, time series 1 has higher variance"
] | No, time series 2 has higher variance | multiple_choice | [
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-0.0... | [
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-0.09259854764547289,
-0.17534198781502194,
-0.1490935578072522,
-0.4196217912346033,
-0.444... | 93 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Red Noise",
"Variance"
] | Random walk is a time series model where the next value is a random walk from the previous value. Variance refers to the distance of the values from the previous steps. At a high level, you should check the distance of the values from the previous steps for both time series. | Similarity Analysis | Distributional | 233 | null |
What is the primary cyclic pattern observed in the time series? | [
"SawtoothWave",
"SquareWave",
"SineWave",
"No Pattern at all"
] | SquareWave | multiple-choice | null | null | 15 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Sawtooth Wave"
] | Check the overall shape of the time series against the definition of provided concepts | Pattern Recognition | Cycle Recognition | 234 | [
-0.12285091032997558,
1.294985682887396,
1.4098372225558853,
1.3899665317435304,
1.5517342300012986,
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1.4645576038859827,
1.5086520760564417,
1.5240763268311674,
1.4603720154375308,
1.3724214772709133,
1.390001... |
The following time series has a noise component, a trend component, and a cyclic component. Is the noise component more likely to be a white noise or random walk? | [
"White Noise",
"Random Walk"
] | White Noise | binary | null | null | 52 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Red Noise",
"Gaussian White Noise"
] | White noise is a stationary process with a constant mean and variance. You should check if the time series has a constant mean and variance over time. This can help you distinguish between white noise and random walk. | Noise Understanding | White Noise Recognition | 235 | [
-0.11389145851128224,
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1.5791780167999492,
1.6106193927932217,
1.3324000414838628,
1.7451063053244278,
1.570962... |
Does time series 1 granger cause time series 2? | [
"No, time series 2 granger causes time series 1",
"No, they are not granger causal",
"Yes, time series 1 granger causes time series 2"
] | No, time series 2 granger causes time series 1 | binary | [
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1.135... | [
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0.07944657776868363,
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-0.008933034287791491,
-0.01239134090091... | 101 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Granger Causality"
] | Granger causality is a statistical concept that determines whether one time series can predict another. While you cannot perform the statistical test, you can check if one time series can predict the other by shifting the time series by a certain number of steps. Do they look simiar after the shift? | Causality Analysis | Granger Causality | 236 | null |
Is the noise in the time series more likely to be additive or multiplicative to the signal? | [
"Multiplicative",
"Additive"
] | Multiplicative | binary | null | null | 57 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Additive Composition",
"Multiplicative Composition",
"Gaussian White Noise"
] | Additive noise is added to the signal, while multiplicative noise is multiplied to the signal. When a trend component is added with a white noise, the general trend still remains. When a trend component is multiplied with a white noise, the noise is amplified. Can you check if it is the case for the given time series? | Noise Understanding | Signal to Noise Ratio Understanding | 237 | [
0,
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-0.013399541860527324,
-0.0025445858367516582,
-0.02231422220272014,
-0.08793070839704334,
-0.05988263516846... |
What is the primary cyclic pattern observed in the time series? | [
"No Pattern at all",
"SineWave",
"SawtoothWave",
"SquareWave"
] | SquareWave | multiple-choice | null | null | 15 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Sawtooth Wave"
] | Check the overall shape of the time series against the definition of provided concepts | Pattern Recognition | Cycle Recognition | 238 | [
-0.11347601184089284,
1.4368026586452634,
1.482879580197419,
1.1946662129256949,
1.348631529801498,
1.530021671072993,
1.3628119343718623,
1.3328810611669664,
1.4018317985616164,
1.604115169341517,
1.528335407771435,
1.4783652897648398,
1.4278422920607294,
1.5134046501598513,
1.518169760... |
The given timeseries is a combination of trend, seasonality and noise. Can you identify the pattern despite the noise? | [
"Yes, Linear Trend and Sine Wave",
"Noise dominated"
] | Yes, Linear Trend and Sine Wave | multiple_choice | null | null | 13 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Sine Wave",
"Gaussian White Noise"
] | Identify which component (trend, seasonality, or noise) has the largest impact on the overall pattern. | Pattern Recognition | Trend Recognition | 239 | [
0.012134599754288863,
1.4510565139317575,
1.0346831445662634,
1.5043901543188234,
2.526900720651428,
1.90688308704727,
2.634932139805409,
2.1161879109536184,
2.7679403890433107,
3.0640521220690937,
3.1304693262109278,
4.211012383045392,
2.7872254768448887,
3.7137549061976807,
3.730517926... |
The given time series is a swatooth wave followed by a square wave. What is the most likely period of the swatooth wave? | [
"54.71",
"14.74",
"31.93"
] | 31.93 | multiple-choice | null | null | 25 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sawtooth Wave",
"Square Wave",
"Period"
] | The sawtooth wave comes before the square wave. Begin by identifying where the sawtooth wave starts. Next, measure the time interval between two peaks. | Pattern Recognition | Cycle Recognition | 240 | [
-2.6347174479222515,
-2.6042032461730034,
-2.339189195854985,
-2.1406582414446755,
-1.9961010238130863,
-2.032061001826381,
-1.877764483857105,
-1.5596543715392446,
-1.4514352117706653,
-0.9685791317423252,
-0.9828399742891905,
-0.8824251310906688,
-0.7512124916006611,
-0.37764328610934006... |
Is time series 2 a lagged version of time series 1? | [
"Yes",
"No, they do not share similar pattern",
"No, time series 1 is a lagged version of time series 2"
] | No, time series 1 is a lagged version of time series 2 | multiple_choice | [
-0.03333616581290451,
-0.014180476245172272,
-0.025375695339209796,
-0.022422580769435176,
0.006909481788711574,
0.007800876550899763,
0.0028238523368027315,
0.010856405735701392,
0.014603779365260736,
0.018240234280711833,
0.01315160443313683,
0.00957440397976289,
-0.007569432490272093,
0... | [
0.000825606294457116,
0.0015603713703385659,
-0.007259843402096845,
-0.004176848554968925,
-0.013858765873761636,
0.012119588450160682,
0.007964441220340027,
-0.017873733375541535,
-0.007217687491225809,
0.001269933999953319,
0.006109683103170684,
0.006960110665817434,
0.025815980942334844,
... | 96 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Lagged Pair"
] | Focus on the time delay between the two time series. If time series 2 is a lagged version, then it should look the same to time series 1 after being shifted by a certain number of steps. Can you check this? | Causality Analysis | Granger Causality | 241 | null |
Is the given time series likely to be stationary after removing the trend? | [
"Yes",
"No"
] | No | binary | null | null | 34 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity",
"Linear Trend",
"Exponential Trend"
] | Trend brings the overall shape of the time series up or down. Assume this effect is removed, does the time series satisfy the stationarity condition? | Pattern Recognition | Stationarity Detection | 242 | [
1.0022264822008116,
1.221096553117398,
1.646727344366883,
1.7591034065970952,
1.8099423118793168,
2.148915717398394,
2.390653331692741,
2.6224706433814395,
2.735653666351133,
2.8206985469688752,
2.752650391312169,
2.542891260954947,
2.4339866238543246,
2.451295184458712,
2.33730104596944... |
Does time series 1 granger cause time series 2? | [
"Yes, time series 1 granger causes time series 2",
"No, they are not granger causal",
"No, time series 2 granger causes time series 1"
] | No, they are not granger causal | binary | [
0.03284838254682385,
-0.5506047449709902,
-0.6686630278899454,
-0.9962763078630587,
-1.1574635324098135,
-1.1846013203723516,
-1.3126892946421025,
-1.288840758438698,
-1.0629797316850738,
-1.069572047856185,
-0.9007614797234799,
-0.6238041646326379,
-0.6554733698113456,
-0.2646516382133298... | [
-0.014595081573314206,
0.024446788718381452,
-0.029823437959574216,
-0.1236891558498755,
-0.19305180387037305,
-0.15527498540908635,
-0.17446174525501382,
-0.14169483887328713,
-0.21797806882155105,
-0.22567997487886568,
-0.2939301758292098,
-0.24727581497692736,
-0.3236876520821757,
-0.41... | 101 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Granger Causality"
] | Granger causality is a statistical concept that determines whether one time series can predict another. While you cannot perform the statistical test, you can check if one time series can predict the other by shifting the time series by a certain number of steps. Do they look simiar after the shift? | Causality Analysis | Granger Causality | 243 | null |
The given time series is a sine wave. What is the most likely amplitude of the sine wave? | [
"1.51",
"6.24",
"17.38"
] | 1.51 | multiple-choice | null | null | 21 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Amplitude"
] | Check the distance between the peak and the baseline. | Pattern Recognition | Cycle Recognition | 244 | [
0.07930227953789715,
0.04370999161429587,
0.3330425443781016,
0.5957463191254064,
0.6801526432773832,
0.7927329802226264,
0.895048357697546,
1.2759589332765224,
1.2173831998912699,
1.3736574412316582,
1.4831058626568128,
1.5426826694874323,
1.4853100247330353,
1.45824409074091,
1.4218282... |
Which of the following best describe the cycle pattern in the given time series? | [
"Amplitude remain the same over time",
"Amplitude increase over time",
"Amplitude decrease over time"
] | Amplitude increase over time | multiple-choice | null | null | 28 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Amplitude"
] | Check the distance between the peak and the baseline, and see how it changes over time. | Pattern Recognition | Cycle Recognition | 245 | [
-0.15027398552418342,
0.6026818592257053,
0.8817376614889434,
1.1714462098174694,
1.5380015361215664,
1.8275073765310508,
1.6574789417348044,
1.837450473148708,
1.9160336777826545,
1.6485919046155106,
1.221247650792107,
0.9931733872285011,
0.7206941034466485,
0.10464958758647372,
-0.0727... |
What is the most likely linear trend coefficient of the given time series? Linear trend coefficient here refers to the end value of the linear trend. | [
"0",
"1.1",
"8.96"
] | 1.1 | multiple_choice | null | null | 2 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend"
] | The bigger the slope of the line, the higher the trend coefficient. | Pattern Recognition | Trend Recognition | 246 | [
0.08353006402066522,
-0.07902902536683586,
0.09057197802588386,
-0.09708405052269962,
-0.06322986480788456,
-0.1546480578106834,
-0.15685729392337572,
-0.09396058720402721,
0.0057932654218428616,
-0.10003364335970696,
0.1761991650542611,
-0.09108645063507471,
0.03326139019842908,
0.1598012... |
Piece-wise stationarity means a time series is stationary in distinct segments, with abrupt changes between segments. Each segment has its own constant statistical properties. Does the time series exhibit piecewise stationarity? | [
"Yes",
"No"
] | Yes | binary | null | null | 38 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity",
"Linear Trend",
"Gaussian White Noise"
] | Look for segments of the time series that are individually stationary, even if the whole series is not. | Pattern Recognition | Stationarity Detection | 247 | [
3.6533643409594743,
3.796996387867786,
3.6446830910143087,
3.649317310638265,
3.560767057398227,
3.5933167744939523,
3.7218362526408932,
3.6286780515410735,
3.5804897351830807,
3.613654494994867,
3.7270549917417704,
3.599785004421119,
3.3991177606742196,
3.6830450986124372,
3.53276508092... |
Are the given two time series likely to have the same underlying distribution? | [
"Yes, they have the same underlying distribution: AR(1)",
"No, they have different underlying distribution: AR(1) and MA(5)"
] | No, they have different underlying distribution: AR(1) and MA(5) | binary | [
1.1362525865772213,
-9.104553563292043,
-7.740233329494264,
-16.8662105269161,
0.5141106612977904,
-0.45229981910541284,
14.050667912404702,
24.493011405704223,
23.566883095360026,
10.039203516513128,
-16.753343182441984,
-2.858363932039129,
7.849667276668541,
18.186743403721987,
13.2657... | [
6.699007160482387,
9.883803280107628,
10.665851387975373,
12.333746754568903,
9.250545396796387,
9.79517275828183,
7.747241008014713,
10.521826230618833,
10.397344056382991,
11.074090933633036,
9.9613849193138,
12.183608464021379,
9.997818946543113,
10.018892583462767,
7.847998969779351,... | 92 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"AutoRegressive Process",
"Moving Average Process"
] | The difference between AR(1) and MA(1) is that AR(1) is a linear combination of past values and white noise, while MA(1) is a linear combination of past white noise values. You should check if the time series exhibit any dependency on the previous values. This could give you a clue about whether the time series is AR(1) or not. Check this for both time series. | Similarity Analysis | Distributional | 248 | null |
Does the trend of the time series change sign or direction at any point? | [
"Yes",
"No"
] | No | binary | null | null | 12 | medium | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend"
] | Check if the overall direction of the time series changes at any point. | Pattern Recognition | Trend Recognition | 249 | [
-0.08450884370791134,
0.11124483019237187,
0.07216125962061659,
0.2351911394080423,
0.05595803072953315,
0.0019847605970916833,
0.21036507088099804,
-0.08260508841921543,
-0.0639766972979449,
-0.11950286810912641,
0.08009508190682484,
0.02237598257486744,
-0.0006330766766439629,
0.02194359... |
Based on the given time series, how many different regimes are there? | [
"1",
"4",
"3"
] | 4 | multiple_choice | null | null | 40 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Regime Switching"
] | First identify the different patterns in the time series. It might be helpful to identify their individual starting and ending points. Then, count the number of different patterns. | Pattern Recognition | Regime Switching Detection | 250 | [
-0.05977982534133484,
0.1314317415113735,
0.048222426060099126,
-0.03812570721388854,
-0.013495035533129762,
-0.004148326921310504,
-0.0434499825788072,
0.01710445198895466,
0.024446196073782914,
-0.023820131474849307,
-0.006051483663179542,
0.1510503455138354,
0.025508029177316302,
0.0945... |
The time series shows a structural break. What is the most likely cause of this break? | [
"Change in variance in underlying distribution",
"Sudden shift in trend direction",
"Abrupt frequency change"
] | Sudden shift in trend direction | multiple_choice | null | null | 71 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Gaussian White Noise",
"Sine Wave"
] | You know the time series shows a structural break. Can you first identify the place where the break happens? Then, you should check the type of break based on the given options. | Anolmaly Detection | General Anomaly Detection | 251 | [
0.10536123992436602,
-0.057482951335859046,
0.041802339721286275,
0.19934699815640194,
0.03568380573571492,
0.09354012417537186,
0.18726532222076603,
0.029820354839024815,
-0.09724939692256133,
0.15932733133176466,
0.24750021244749779,
0.02835204857367013,
0.2453819443417709,
0.26091992289... |
The given time series is a sine wave. What is the most likely amplitude of the sine wave? | [
"2.33",
"5.17",
"15.69"
] | 2.33 | multiple-choice | null | null | 21 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Amplitude"
] | Check the distance between the peak and the baseline. | Pattern Recognition | Cycle Recognition | 252 | [
0.14562527548957302,
0.40249066177411236,
0.7890084412988563,
0.8368602564947466,
1.0336650282045703,
1.577192659787613,
1.6462486395099276,
1.8529348928717877,
2.0525483648622442,
2.1643830837297515,
2.1546748347595313,
2.4021610604180985,
2.422493355836586,
2.2390025255494135,
2.151446... |
You are given two time series with same underlying pattern but different noise level (variance). Which time series has higher magnitude of noise? | [
"Time series 1",
"Time series 2"
] | Time series 2 | multiple_choice | [
1.0993065409667435,
1.324368188901164,
1.7255022044621973,
2.147789937324411,
2.5490877313448257,
2.8754391721087806,
3.067243503506616,
3.3847060322829345,
3.3899960131844398,
3.6991171027058924,
3.9605799344285932,
3.949968557960749,
3.8996840889862465,
3.961409614327855,
3.93717597200... | [
-0.5907777232294664,
-1.9916413106783797,
1.4003390319490183,
0.898980954126422,
0.0023579363246852836,
-1.6386481368099801,
3.11094191166671,
2.6443421119228585,
0.5669345031814701,
1.94727148523919,
3.795173623998176,
3.844082523814507,
-1.1054715891078377,
7.117980631327687,
4.8641405... | 60 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Exponential Trend",
"Gaussian White Noise",
"Variance"
] | When the noise level is high, it can distort the pattern in the time series. Both time series have the same underlying pattern, but different noise level. To tell which time series has higher noise level, you should check the degree of distortion of the time series pattern. | Noise Understanding | Signal to Noise Ratio Understanding | 253 | null |
Which of the given time series has higher variance? | [
"Time Series 1",
"Time Series 2"
] | Time Series 1 | multiple_choice | [
-2.2986534527013247,
-1.4673425005691627,
-1.8681896015316688,
4.132097330866706,
3.3823311542163648,
-3.0500595081574198,
-0.49821959246016,
0.15136824038844998,
1.3645440136212792,
-1.1489958115106778,
2.432544566107376,
-4.769403760273353,
-3.6703352844436123,
6.475485083595093,
3.420... | [
0.08276053818564745,
-0.06099273364265922,
-0.04306521065867642,
-0.09440297530039454,
-0.05294411770145911,
0.06448537796420806,
-0.05848977189346817,
0.2785527830275105,
-0.12147368198227348,
0.25101823091404013,
-0.26764023194641423,
-0.04699832101564346,
0.19170528061591474,
-0.0329894... | 44 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Variance"
] | Check the degree of variation of the time series over time. | Pattern Recognition | First Two Moment Recognition | 254 | null |
The given time series has multiple trends followed by each other, what is the correct ordering of the trend components? | [
"Exponential -> Linear -> Log",
"Linear -> Exponential",
"Log",
"Linear -> Exponential -> Log"
] | Exponential -> Linear -> Log | multiple_choice | null | null | 9 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend",
"Log Trend"
] | Identify the different components first, and then check the assignment of each component. | Pattern Recognition | Trend Recognition | 255 | [
1.04868374822586,
1.0357759295680895,
0.8285013359765673,
1.1680955973932299,
0.8106520165642396,
0.879783997577739,
1.0346190010044691,
1.0788993323979634,
1.028314032896598,
1.0096313599236386,
1.0629481629974575,
1.131279510204187,
0.971348484553767,
0.9820636346932873,
1.014481785400... |
Is the given time series strictly stationary? | [
"Yes",
"No"
] | No | binary | null | null | 30 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity"
] | Try to see if the time series has a constant mean, and degree of variation over time. | Pattern Recognition | Stationarity Detection | 256 | [
-2.575219909931604,
-2.372552073993247,
-2.2395228282867214,
-1.992306079478577,
-1.840512305287538,
-1.7836403437391277,
-1.528652914500403,
-1.2108869484964684,
-1.1265248263883472,
-0.6475786341478263,
-0.6067681872758859,
-0.5584009352121836,
-0.1819974899397624,
0.08238116535962946,
... |
The given time series is a swatooth wave followed by a square wave. What is the most likely period of the swatooth wave? | [
"36.38",
"15.94",
"55.43"
] | 15.94 | multiple-choice | null | null | 25 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sawtooth Wave",
"Square Wave",
"Period"
] | The sawtooth wave comes before the square wave. Begin by identifying where the sawtooth wave starts. Next, measure the time interval between two peaks. | Pattern Recognition | Cycle Recognition | 257 | [
-2.655805523658714,
-2.2212418001689502,
-1.9749217621609345,
-1.6182993189685204,
-1.1219025937154843,
-1.0301677127310263,
-0.5546885018325032,
-0.40086622984806014,
0.05114328211758113,
0.44582360257856424,
0.590350957363275,
0.9430997939257212,
1.2680834815584199,
1.5482102000130677,
... |
Two time series are given. Both of them have a noise component. Do they have the same type of noise? | [
"No, they have different noise: white noise and red noise",
"Yes, they both have Gaussian white noise"
] | No, they have different noise: white noise and red noise | binary | [
-0.8708327586181327,
0.32698056545247955,
0.38598401044642205,
2.1762551262894214,
3.23875249815833,
3.1484046515765534,
1.3855491650923248,
3.0566100992446654,
1.7348526037056178,
0.800262663811486,
3.722359022718303,
1.652225412812463,
-0.582374785237068,
-0.1215552762225253,
-0.696930... | [
-0.0835148303300472,
0.10337646448445575,
0.43574840861462866,
0.737478052572563,
1.0384482303879903,
1.2579593567091614,
1.39004270959362,
1.4774180002142703,
1.6853340565532655,
1.7535549366332497,
1.5383162515542028,
1.5972890876280013,
1.5071268672386573,
1.289885502986826,
1.0335153... | 87 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Gaussian White Noise",
"Red Noise",
"Additive Composition"
] | When a white noise is added to a time series, it is expected the random fluctuations have similar amplitude or distribution. Random walk, on the other hand, can result in very different noise patterns. | Similarity Analysis | Shape | 258 | null |
Are the given two time series likely to have the same underlying distribution? | [
"Yes, they have the same underlying distribution",
"No, they have different underlying distribution"
] | Yes, they have the same underlying distribution | binary | [
-0.060127618693246176,
-0.03930163313754402,
0.13632355228756862,
0.03883851448952098,
0.027506994440332833,
-0.04662883163321536,
0.04384489341091617,
-0.15080423701935947,
-0.056151720139368796,
-0.07054944722522014,
-0.09725402065536565,
-0.254850996084007,
-0.13266356672974794,
-0.1275... | [
-0.06810215345230355,
-0.08761890770482698,
-0.037446295341647576,
-0.29142597030292233,
-0.029017392579662432,
-0.08254751898923046,
-0.2936376207324496,
-0.1747861289645654,
-0.11947420029583106,
-0.2888609249018274,
-0.1813982267323606,
-0.2044469909525123,
-0.3447277256376221,
-0.10934... | 95 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Red Noise",
"AutoRegressive Process",
"Linear Trend"
] | When we say two time series have the same underlying distribution, you should check if they have the same mean and variance. They should also share similar behaviors over time. | Similarity Analysis | Distributional | 259 | null |
You are given two time series which both have trend components. Do they have the same type of trend? | [
"No, time series 1 has exponential trend and time series 2 has log trend",
"Yes, they both have exponential trend",
"No, time series 1 has linear trend and time series 2 has exponential trend"
] | No, time series 1 has exponential trend and time series 2 has log trend | multiple_choice | [
1.066907872787167,
1.382133309045304,
1.6503852267722692,
2.0889012422644293,
2.3982026011889173,
2.5596641352559626,
2.6403662123694587,
2.8332182775767736,
2.7616902129537046,
2.51913957656919,
2.4231573626331206,
2.131973126846004,
1.7061341587606638,
1.5755831473899324,
1.22082677426... | [
-0.08807362752601712,
0.23793256495718887,
0.7207877086738615,
1.0032758788562126,
1.193233241732223,
1.5446625844091986,
1.612624640024284,
1.6136065113271276,
1.6648321597346536,
1.4478911081382961,
1.4610214431695983,
1.2625671850897187,
0.8464715978937635,
0.394494642480804,
0.127207... | 85 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend",
"Log Trend"
] | First identify the trend component for each time series. Then, check if they are equal. | Similarity Analysis | Shape | 260 | null |
Is the given time series likely to be a random walk process? | [
"Yes",
"No"
] | No | binary | null | null | 53 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Red Noise"
] | Random walk is a non-stationary process with a constant mean and variance. You should check if the time series has a constant mean and variance over time. Another important property is that the noise is correlated over time. Does the time series seem to have these properties? | Noise Understanding | Red Noise Recognition | 261 | [
1.8865370914060906,
1.0508917095287262,
3.4986233670891087,
-1.3437212218522125,
-0.7825850419501692,
0.19317529608222167,
-2.7611867563034975,
2.8219256960184125,
0.39314340629555616,
-1.7464899581359146,
-1.1646647884083112,
-0.8864136729187402,
2.9503941387964767,
2.32199944138035,
-1... |
Two time series are given with different cyclic components. Which time series has a higher period of the cyclic component? | [
"Time series 2 has higher period",
"Time series 1 has higher period"
] | Time series 1 has higher period | binary | [
0.1285784297491742,
0.016928429544453977,
0.3367983981445066,
0.4653488986990382,
0.6162596710831243,
0.663631331228839,
0.7191806087946149,
1.0889614718474419,
0.8473014037890185,
1.078882032892279,
1.0666915784650706,
1.1418480060397378,
1.1684006497073474,
1.037540931634124,
1.0066766... | [
0.0007438521800432031,
1.2291809336952662,
1.9526396885469612,
2.098680733652001,
1.623647820050104,
0.6413064459585432,
-0.6290564001199184,
-1.4574931056293996,
-2.0167581829528305,
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-1.2094233562403656,
-0.06402985166346666,
1.0753740195720425,
1.967707562032813,
2.... | 84 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Period"
] | Period refers to the length of one cycle in the cyclic component. You should check the distance between two peaks or two troughs for both time series. | Similarity Analysis | Shape | 262 | null |
What type of trend does the time series exhibit in the latter half? | [
"No trend",
"Linear",
"Exponential"
] | Exponential | multiple_choice | null | null | 14 | medium | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend"
] | Focus on the pattern of growth or decline in the second half of the time series. | Pattern Recognition | Trend Recognition | 263 | [
-0.0064871480989294045,
-0.009894281309644752,
0.014577177575064501,
-0.002697587765990661,
0.016173034570682095,
-0.0073048399034164775,
0.013732262265389,
0.02583402948204392,
-0.005471091157551577,
0.014447317948725145,
0.005219435814595153,
0.0012752755979812402,
0.04219721556670805,
0... |
The given time series is a sine wave followed by a square wave patterns with different amplitude. How does the amplitude vary over time? | [
"Increase",
"Remain the same",
"Decrease"
] | Decrease | multiple-choice | null | null | 19 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Amplitude"
] | Focus on the amplitude instead of cyclic pattern change, check if the distance between the peak and the baseline changes. | Pattern Recognition | Cycle Recognition | 264 | [
-0.016498872701190104,
1.5128303229000324,
2.4977456927631687,
3.6657809394183736,
4.624808701126883,
5.293119753451111,
5.712840785212982,
5.8268953607957865,
5.499374272245703,
5.048994847488324,
4.333720816466572,
3.4489062554924343,
2.044355205053812,
1.2619102171908216,
-0.156994521... |
Does the given time series exhibit any monotonic increasing trend? | [
"Yes",
"No"
] | Yes | binary | null | null | 3 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend",
"Log Trend"
] | Check if the time series values increase over time. | Pattern Recognition | Trend Recognition | 265 | [
0.30603428813990025,
-0.08202467168411891,
-0.21840430441450995,
-0.09072170015875074,
-0.08904154461635153,
-0.08671597987781181,
0.11748868844068626,
0.05000481024122326,
-0.12771063554170203,
0.1829142881195672,
0.24208776674014235,
-0.19426018810967582,
0.1574711045232093,
-0.005233939... |
You are given two time series following similar pattern. Both of them have an anomaly. Do they have the same type of anomaly? | [
"Yes, Time series 1 and time series 2 both have cutoff anomaly",
"No. They have different types of anomalies: cutoff and spikes"
] | No. They have different types of anomalies: cutoff and spikes | binary | [
0,
1.3197700656850027,
2.3475901594841133,
2.8561113489940353,
2.73289222792819,
2.0052793149058554,
0.8343559703928852,
-0.5207012187513865,
-1.759971569375138,
-2.609155055449791,
-2.8802678673025808,
-2.5132384064108586,
-1.5891995891583526,
-0.31253621371525214,
1.0343401527814622,
... | [
-5.775990863885145,
0.5582175377966975,
1.0594875534668133,
2.0599514125327216,
1.6979356410640847,
2.825292387697937,
1.6630704659342566,
-0.9390209468910473,
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0.46120546752741515,
-0.09332977354327984,
-2.9588429025346907,
3.23405941100128,
3.7276768931872697,
-3.73... | 75 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Cutoff Anomaly",
"Spike Anomaly"
] | For each time series, identify the type of anomaly based on the given definitions. Then, check if they have the same type of anomaly. | Anolmaly Detection | General Anomaly Detection | 266 | null |
You are given two Autoregressive processes AR(1). Which of the following time series has higher standard deviation for their random component? | [
"Time series 2",
"Time series 1"
] | Time series 1 | multiple_choice | [
1.8675319337925744,
-0.746908847382465,
12.620291933277358,
13.543444247714593,
-3.7064484054764524,
-3.879050638303002,
-0.803132019132359,
-7.669548987900008,
0.5469151179965106,
-4.164859612765487,
-22.262507433729247,
-16.333864878208825,
-3.9850509747142127,
-6.104026700512721,
-19.... | [
-1.4033878175517729,
0.16206156465619204,
1.2795061009599942,
2.0289373493976717,
0.2819402230772041,
-0.7534124911496793,
-1.0602501945680034,
0.32334649942402627,
-0.43405730797198344,
1.0307027758306517,
-0.018446961762832337,
-0.252021979530287,
-0.06315913997976721,
-0.047402021088801... | 61 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"AutoRegressive Process",
"Variance"
] | The standard deviation of the noise component is related to the average distance between the data points and their past values. You should check the degree of variation of the time series over time. Which time series has a higher change in average? | Noise Understanding | Signal to Noise Ratio Understanding | 267 | null |
Does the given two time series have similar pattern? | [
"No, they have different seasonable pattern",
"Yes, they have similar seasonal pattern"
] | No, they have different seasonable pattern | binary | [
0,
0.15861821364668247,
0.31459735044942755,
0.46534224230862214,
0.6083448080632111,
0.7412257827395818,
0.8617743035672747,
0.9679846941326344,
1.058089834658667,
1.1305905631991169,
1.1842806185721142,
1.2182667100348927,
1.2319833797819881,
1.2252024109858795,
1.1980366248498688,
1... | [
0,
5.9419001521320585,
5.9419001521320585,
5.9419001521320585,
5.9419001521320585,
5.9419001521320585,
5.9419001521320585,
5.9419001521320585,
5.9419001521320585,
5.9419001521320585,
5.9419001521320585,
5.9419001521320585,
5.9419001521320585,
5.9419001521320585,
5.9419001521320585,
5.9... | 78 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave"
] | Pattern refers to the general shape of the time series. In this case, you see both time series have cyclic patterns. Do their behaviors at peak and trough look similar? | Similarity Analysis | Shape | 268 | null |
Is time series 2 a lagged version of time series 1? | [
"No, time series 1 is a lagged version of time series 2",
"Yes",
"No, they do not share similar pattern"
] | No, time series 1 is a lagged version of time series 2 | multiple_choice | [
0.16540170346461877,
0.16302392892348136,
0.1659432462267423,
0.14543808386892879,
0.1590592442563044,
0.11705116252723696,
0.10431959986575151,
0.11642695109099659,
0.11327162452843659,
0.1345029115253836,
0.12298201056078213,
0.1353784952682223,
0.13840290295585453,
0.15004630851640927,
... | [
0.003045375877239472,
0.0007482835931180518,
-0.008630601356586975,
0.00311697856133958,
-0.005452565578972515,
-0.011267502873873464,
0.0016064589645816332,
0.013900348779613795,
0.01770706323041444,
0.04624320269166396,
0.06542240103317566,
0.08883641260875623,
0.07391001798446707,
0.055... | 96 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Lagged Pair"
] | Focus on the time delay between the two time series. If time series 2 is a lagged version, then it should look the same to time series 1 after being shifted by a certain number of steps. Can you check this? | Causality Analysis | Granger Causality | 269 | null |
Is the given time series likely to be stationary after differencing? | [
"Yes",
"No"
] | Yes | binary | null | null | 31 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity"
] | Differencing is a common technique to make a time series stationary. Focus on checking if the trend is removed after differencing. | Pattern Recognition | Stationarity Detection | 270 | [
0,
0.0008649667957462382,
0.0017299335914924764,
0.002594900387238715,
0.003459867182984953,
0.004324833978731191,
0.00518980077447743,
0.006054767570223668,
0.006919734365969906,
0.007784701161716144,
0.008649667957462382,
0.00951463475320862,
0.01037960154895486,
0.011244568344701097,
... |
Is the given time series strictly stationary? | [
"No",
"Yes"
] | Yes | binary | null | null | 30 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity"
] | Try to see if the time series has a constant mean, and degree of variation over time. | Pattern Recognition | Stationarity Detection | 271 | [
1.5963268831680995,
0.704637365680489,
3.101476037501168,
0.18161038994130607,
0.4037768775796521,
1.4048146756224864,
-0.20788730666674987,
1.9334670190123786,
3.084629139249161,
1.14883233544097,
3.5047178188178023,
2.7086086808690646,
-1.8883167166075236,
1.594884711508574,
-0.7089454... |
Does the trend of the time series change sign or direction at any point? | [
"No",
"Yes"
] | Yes | binary | null | null | 12 | medium | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend"
] | Check if the overall direction of the time series changes at any point. | Pattern Recognition | Trend Recognition | 272 | [
-0.02356524261414575,
0.11541728029623982,
-0.009235134633152287,
-0.005616629481838544,
-0.06824819699938356,
0.1831554926297393,
0.1159893465067099,
0.2096602275680352,
-0.010206027652800492,
-0.10638315654786637,
-0.23976818383361664,
0.13869586281577864,
0.044865147181686466,
0.0162938... |
The given time series is a sine wave. What is the most likely amplitude of the sine wave? | [
"5.87",
"2.45",
"16.58"
] | 16.58 | multiple-choice | null | null | 21 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Amplitude"
] | Check the distance between the peak and the baseline. | Pattern Recognition | Cycle Recognition | 273 | [
0.026435871664162225,
2.947381791043848,
5.744388622339012,
8.299803022293263,
10.747907634872892,
12.638417726658053,
14.432525729366487,
15.42786307182692,
16.452064504820893,
16.555815363274316,
16.22370079472146,
15.511449691123296,
14.191170029180814,
12.600433634336454,
10.37539891... |
Based on the given time series, how many different regimes are there? | [
"1",
"4",
"3"
] | 3 | multiple_choice | null | null | 40 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Regime Switching"
] | First identify the different patterns in the time series. It might be helpful to identify their individual starting and ending points. Then, count the number of different patterns. | Pattern Recognition | Regime Switching Detection | 274 | [
-0.18518213384366872,
-0.016735941555914194,
0.19151782302497253,
0.03523841127805205,
0.13406511993189318,
0.0512254182190864,
-0.04579754299759139,
-0.0034601177976552756,
0.15398768200139012,
-0.08768159905790567,
-0.03972160310488085,
-0.045176015557314764,
0.09358012597879412,
0.24118... |
The following time series has an anomaly where the pattern is cutoff at certain point in time. What is the likely pattern of the time series without the anomaly? | [
"Sawtooth wave with exponential trend",
"Square wave with log trend",
"Sine wave with linear trend"
] | Square wave with log trend | multiple_choice | null | null | 67 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Sawtooth Wave",
"Square Wave",
"Linear Trend",
"Log Trend",
"Cutoff Anomaly"
] | Cutoff anomaly brings sudden disappearance of the pattern. However, this only influences a small part of the time series. Can you check the place where the pattern disappears and try to recover the original pattern? | Anolmaly Detection | General Anomaly Detection | 275 | [
0,
1.5928186944440248,
1.5986271046198148,
1.6044019719039477,
1.6101436814856431,
1.6158526119570114,
1.6215291354628483,
1.6271736178462053,
1.6327864187898722,
1.6383678919539113,
1.6439183851093766,
-1.5245144586038135,
-1.5190249050618057,
-1.5135653222667218,
-1.508135384738383,
... |
The given time series has multiple trends followed by each other, what is the correct ordering of the trend components? | [
"Log",
"Exponential -> Linear -> Log",
"Linear -> Exponential",
"Linear -> Exponential -> Log"
] | Linear -> Exponential | multiple_choice | null | null | 9 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Exponential Trend",
"Log Trend"
] | Identify the different components first, and then check the assignment of each component. | Pattern Recognition | Trend Recognition | 276 | [
0.06564007484010428,
-0.15988944628604046,
-0.06531823016744667,
-0.016077867997717647,
-0.08456535320646111,
-0.01833610846465223,
0.2981090186056465,
0.22539272181173453,
-0.08098110658077348,
0.02606299731014177,
0.021977731271719488,
-0.0025561580568583195,
-0.06670745319789403,
0.0600... |
Is the given time series likely to have a non-stationary anomaly? | [
"No, the anomaly is stationary (white noise)",
"Yes, due to trend reversal",
"Yes, due to cutoff"
] | No, the anomaly is stationary (white noise) | binary | null | null | 69 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity",
"Linear Trend",
"Sine Wave",
"Cutoff Anomaly",
"Spike Anomaly"
] | Non-stationary anomaly refers to the anomaly that changes over time. You should check if the time series has a constant mean and variance over time. If not, you should check the type of anomaly based on the given definitions. For example, spikes anomaly are stationary. | Anolmaly Detection | General Anomaly Detection | 277 | [
6.682393141981098,
0.09336836706943086,
-1.6951824868553198,
1.402941441790233,
1.5522407687064226,
1.4877256566757673,
-4.950123871178498,
8.351054847853556,
-1.2913569780223924,
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1.6316182684994343,
-12.607834844195978,
-1.3818081822149706,
-1.365779003811748,
-1.139... |
You are given two time series where one is the lagged version of the other. What is the most likely lagging step? | [
"Lagging step is between 30 to 45",
"Lagging step is between 60 to 75",
"Lagging step is between 5 to 10"
] | Lagging step is between 60 to 75 | multiple_choice | [
-0.022245977067615524,
-0.047069558822787544,
-0.04304375072185169,
0.03461584157999469,
0.05091838175952946,
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0.03583380782524495,
0.002580506222015881,
0.06595376290887112,
0.023084291803394108,
-0.013597099526170303,
-0.0371... | [
-0.1083787969630274,
-0.08288806707210264,
-0.035683925760824256,
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0.03333794286096474,
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0.15631188976721508,
0.14218879555844893,
0.20709566975106494,
0.3402041743331129,
0.3288556151759021,
0.39038274892262... | 98 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Lagged Pair"
] | You already know that one time series is the lagged version of the other. Shift the time series by lags proposed in the options and check which one looks the same as the other time series. | Causality Analysis | Granger Causality | 278 | null |
Based on the given time series, how many different regimes are there? | [
"4",
"1",
"3"
] | 1 | multiple_choice | null | null | 40 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Regime Switching"
] | First identify the different patterns in the time series. It might be helpful to identify their individual starting and ending points. Then, count the number of different patterns. | Pattern Recognition | Regime Switching Detection | 279 | [
-0.12371821696459087,
-0.023095773750561,
0.22996248488228094,
-0.09835937411939451,
0.01691257178950211,
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0.06136749348370424,
0.11804724302198152,
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0.05101344363081014,
0.1505233427994819,
-0.14747987377473903,
-0.1979228072567833,
-0.014161359757... |
Are the given two time series likely to have the same underlying distribution? | [
"Yes, they have the same underlying distribution: AR(1)",
"No, they have different underlying distribution: AR(1) and MA(5)"
] | No, they have different underlying distribution: AR(1) and MA(5) | binary | [
-13.681361434465327,
-18.526480174566704,
-21.724927935639947,
-19.721563810838834,
-32.47995289614606,
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-1... | [
9.98896177033827,
10.869735151526468,
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9.11540939184784,
8.088785914303354,
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8.115301445426367,
9.648480480706347,
9.261944914580102,
7.574150924459055,
11.33196046943974,
... | 92 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"AutoRegressive Process",
"Moving Average Process"
] | The difference between AR(1) and MA(1) is that AR(1) is a linear combination of past values and white noise, while MA(1) is a linear combination of past white noise values. You should check if the time series exhibit any dependency on the previous values. This could give you a clue about whether the time series is AR(1) or not. Check this for both time series. | Similarity Analysis | Distributional | 280 | null |
Does the given time series exhibit regime switching? | [
"Yes",
"No"
] | Yes | binary | null | null | 39 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Regime Switching"
] | Identify whether the time series exhibit different patterns over time. | Pattern Recognition | Regime Switching Detection | 281 | [
-0.0013426890601733917,
0.035343624445322186,
-0.007661286162485493,
0.16950711367045293,
-0.05820391950323263,
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0.046106372501052165,
-0.05267571757817057,
-0.034168472295538266,
-0.04781515673850502,
0.27933432341768405,
0.05668121114219711,
0.0572669549183324,
-0.100... |
The given time series is a square wave. What is the most likely period of the square wave? | [
"70.86",
"47.53",
"17.79"
] | 70.86 | multiple-choice | null | null | 22 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Square Wave",
"Period"
] | Check the time interval between two peaks. | Pattern Recognition | Cycle Recognition | 282 | [
0.011380933339714731,
1.1983490744855938,
1.2986450295403622,
1.457247969967053,
1.3283756229660364,
1.5062666039224955,
1.3860477779316644,
1.6315029993922456,
1.312512633428724,
1.5445892527684926,
1.3955221111353262,
1.3477175101703065,
1.4260248794405033,
1.303083359501247,
1.4139647... |
Despite the noise, does the given two time series have similar pattern? | [
"No, they have different seasonal pattern",
"Yes, they have similar seasonal pattern"
] | No, they have different seasonal pattern | binary | [
0.08891839901462584,
0.690219208408698,
1.0062309554904745,
1.3897250525665517,
2.4385063397674522,
2.0039133634778157,
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1.583109109598633,
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-0.6235985946675859,
-0.6456299462458452,
-1.45225264997059,
-1.59950443... | [
-0.2330883052442114,
2.6866593487315242,
2.1274706762105784,
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2.3777299942938126,
-2.4743909935184036,
-2.6372759199323204,
-2.8880281743268696,
-2.60... | 79 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave"
] | Noise refers to the random fluctuations in the time series. You should focus on the overall pattern of the time series. Pattern refers to the general shape of the time series. In this case, you see both time series have cyclic patterns. Do their behaviors at peak and trough look similar? | Similarity Analysis | Shape | 283 | null |
The given time series has a trend and a cyclic component. It also has an anomaly. What is the most likely combination of components without the anomaly? | [
"Exponential trend and square wave",
"Log trend and sawtooth wave",
"Linear trend and sine wave"
] | Log trend and sawtooth wave | multiple_choice | null | null | 70 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Sine Wave",
"Exponential Trend",
"Square Wave",
"Log Trend",
"Sawtooth Wave",
"Cutoff Anomaly",
"Flip Anomaly"
] | The anomaly only influences a small part of the time series. You should focus on the overall pattern of the time series without the anomaly. Can you recover the original pattern? | Anolmaly Detection | General Anomaly Detection | 284 | [
-2.46398788362281,
-2.146241822352117,
-1.8285849587092349,
-1.5110156313845722,
-1.193532225053572,
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0.7096489443862742,
1.0265726048883836,
1.343421798315665,
1.6601977934614984,
1.9... |
You are given two AR(1) process, which one of them is more likely to have a larger magnitude in autocorrelation at lag 1? | [
"Time Series 1",
"Time Series 2"
] | Time Series 2 | multiple_choice | [
-11.825519262483743,
-28.114357275497007,
3.596096409787487,
-3.626936359394,
-18.97213438846225,
13.799292019059601,
2.430243433485974,
-23.579714859982523,
-14.130018653425154,
-2.000797941588297,
1.708784636736068,
-13.26086370149596,
-9.255001393924287,
-2.0418958570033734,
7.3600116... | [
30.829673623460806,
39.631024406066146,
42.539997404539875,
16.954594020366475,
17.76141588911635,
-3.8802564754437725,
-7.645220605631263,
-13.7747273786097,
-7.328035963153083,
13.096574064729335,
10.59555729496834,
16.19662166391034,
2.5497634762738066,
-5.719364519025751,
0.756424690... | 47 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Autocorrelation",
"AutoRegressive Process"
] | While it is hard to directly measure the autocorrelation for higher order lags, the autocorrelation at lag 1 can be approximated by observing the time series pattern. You can tell this by checking the sign and magnitude changes at each step compared to the previous step. You should compare the two time series to see which one has a larger magnitude in autocorrelation at lag 1. | Pattern Recognition | AR/MA recognition | 285 | null |
Two time series are given. One has noise and the other does not. Do they have similar pattern? | [
"Yes, they are all Sine Wave",
"No, they have different seasonal pattern: Square Wave and Swatooth Wave"
] | Yes, they are all Sine Wave | binary | [
0.2228808432802557,
0.9036311732447317,
1.1762634377060974,
1.777089481797737,
2.127782013899699,
2.2912029102282716,
2.826304166125728,
2.42219162432348,
2.0985858055919473,
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1.106593318643338,
0.40370021020543495,
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-1.6087729... | [
0,
0.43213156565440025,
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1.2691751613486086,
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3.276721769425299,
3.3837142267211844,
3.437211051353863,
3.4363664725851906,
3.381193842983541,
3.272565... | 82 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Sawtooth Wave"
] | Noise refers to the random fluctuations in the time series. You should focus on the overall pattern of the time series. Pattern refers to the general shape of the time series. In this case, you see both time series have cyclic patterns. Do their behaviors at peak and trough look similar? | Similarity Analysis | Shape | 286 | null |
How does the noise in the given time series influence the detection of periodic pattern in the time series? | [
"Distort the pattern",
"No influence, Sinewave"
] | Distort the pattern | binary | null | null | 58 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Gaussian White Noise",
"Sine Wave",
"Additive Composition"
] | When the noise level is high, it can distort the pattern in the time series. Can you check if you can still detect the cyclic pattern in the time series? | Noise Understanding | Signal to Noise Ratio Understanding | 287 | [
2.1479282686421715,
0.08342401980031322,
2.68969926124376,
1.1732830894109876,
2.639373508439472,
0.16815628818564043,
3.7538693738668747,
-3.152213853471542,
-2.6943990698317926,
0.7775000578743154,
1.4698546563741666,
-1.8259613778166117,
-4.08698262975783,
-2.2395554377082973,
-1.6738... |
The following time series has two types of anomalies appearing at different time points. What are the likely types of anomalies? | [
"Cutoff: the pattern of time series disappeared for certain point in time and became flat and Flip: the pattern is flipped at certain point in time",
"Speedup: the period of cyclic components is different from other parts of the time series and Flip: the pattern is flipped at certain point in time",
"Speedup: t... | Speedup: the period of cyclic components is different from other parts of the time series and Flip: the pattern is flipped at certain point in time | multiple_choice | null | null | 68 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Cutoff Anomaly",
"Flip Anomaly",
"Speed Up/Down Anomaly"
] | You should first identify the two places where the anomalies appear. Then, you should check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 288 | [
0,
0.9493275893697217,
1.7547507379463214,
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2.486586302988083,
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-2.2176794618188054,
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-2.1989158342588064,
-1.655144001992915,
... |
The given time series is a sine wave followed by a square wave. What is the most likely amplitude of the square wave? | [
"7.98",
"16.34",
"1.45"
] | 16.34 | multiple-choice | null | null | 24 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Sine Wave",
"Square Wave",
"Amplitude"
] | After the sine wave, the square wave follows. Begin by identifying where the square wave starts. Next, measure the distance between its peak and baseline. | Pattern Recognition | Cycle Recognition | 289 | [
0.07708921379672275,
0.11214242595334271,
0.37601574583703745,
0.47716421200079484,
0.697670752007289,
0.727046623647165,
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1.0623488573108153,
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1.1471748873514993,
1.3417923204706186,
1.468237186613254,
1.3279006850954245,
1.1568561403610023,
1.43179... |
What is the most likely linear trend coefficient of the given time series? Linear trend coefficient here refers to the end value of the linear trend. | [
"0",
"2.71",
"9.93"
] | 0 | multiple_choice | null | null | 2 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend"
] | The bigger the slope of the line, the higher the trend coefficient. | Pattern Recognition | Trend Recognition | 290 | [
3.841206869646397,
3.8490488742842133,
3.82211202256616,
3.8708167656326617,
3.845213897917691,
3.7858532897533537,
3.88638051156257,
3.8645049242635165,
3.8735113759811193,
3.8504498947566805,
3.7454362994080186,
3.876993153934944,
3.8374215074405127,
3.8486365193129957,
3.8558144608287... |
What type of noise is present in the given time series? | [
"No significant noise",
"Red Noise",
"Gaussian White Noise"
] | Gaussian White Noise | multiple_choice | null | null | 62 | medium | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Gaussian White Noise",
"Red Noise"
] | Observe the pattern of fluctuations in the time series. | Noise Understanding | Signal to Noise Ratio Understanding | 291 | [
1.322085262047744,
-1.882580116977823,
-0.4615387643818145,
0.8368971999411453,
1.4041325302169363,
0.7435830269970892,
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-1.0235232990811909,
-0.2757760358089006,
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-0.5848141840451754,
1.5314334770936207,
-0.35549496848728984,
0.3476183073144933,
-... |
Based on the given time series, how many different regimes are there? | [
"1",
"3",
"4"
] | 1 | multiple_choice | null | null | 40 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Regime Switching"
] | First identify the different patterns in the time series. It might be helpful to identify their individual starting and ending points. Then, count the number of different patterns. | Pattern Recognition | Regime Switching Detection | 292 | [
0.05364238398152217,
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0.03804663222663748,
0.008074687193758309,
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0.03639474568716528,
0.010685829690799259,
-0.04702555879539657,
-0.07100902451050255,
0.04... |
What type of noise is present in the given time series? | [
"Gaussian White Noise",
"Red Noise",
"No significant noise"
] | Red Noise | multiple_choice | null | null | 62 | medium | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Gaussian White Noise",
"Red Noise"
] | Observe the pattern of fluctuations in the time series. | Noise Understanding | Signal to Noise Ratio Understanding | 293 | [
-0.006784442905368261,
-0.001087027632700164,
-0.14475621488754387,
0.14307899180851902,
0.10038456557235645,
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-0.010871967619827727,
0.016310851838512663,
-0.1404645055495806,
-0.21185734852397764,
-0.1... |
You are seeing two instances of random walk. Are they likely to have the same variance? | [
"Yes, they have the same variance",
"No, time series 2 has higher variance",
"No, time series 1 has higher variance"
] | No, time series 1 has higher variance | multiple_choice | [
-0.05629906097900009,
-0.3602676677539888,
-0.2929395909873497,
-0.2891092727065862,
-0.33847393926480257,
-0.32053098082131626,
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-0.35260675102702516,
-0.15150013183830213,
-0.41041702290470977,
-0.3919919231857836,
-0.2607605... | [
0.0016914279368809946,
0.01948573921825744,
0.014580436226686566,
0.03146011454663122,
0.08101108998033374,
0.11124902859936267,
0.06801117651595842,
0.05822800466835454,
0.08102390875677955,
0.06557875714553066,
0.08060601640477891,
0.04720312077689836,
0.02960992053419546,
-0.01179893830... | 93 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Red Noise",
"Variance"
] | Random walk is a time series model where the next value is a random walk from the previous value. Variance refers to the distance of the values from the previous steps. At a high level, you should check the distance of the values from the previous steps for both time series. | Similarity Analysis | Distributional | 294 | null |
The given time series has a trend and a cyclic component. It also has an anomaly. What is the most likely combination of components without the anomaly? | [
"Linear trend and sine wave",
"Exponential trend and square wave",
"Log trend and sawtooth wave"
] | Exponential trend and square wave | multiple_choice | null | null | 70 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Sine Wave",
"Exponential Trend",
"Square Wave",
"Log Trend",
"Sawtooth Wave",
"Cutoff Anomaly",
"Flip Anomaly"
] | The anomaly only influences a small part of the time series. You should focus on the overall pattern of the time series without the anomaly. Can you recover the original pattern? | Anolmaly Detection | General Anomaly Detection | 295 | [
1,
3.466216789685967,
3.4684506637713626,
3.4706895169522514,
3.4729333603265697,
3.4751822050169894,
3.477436062170975,
3.4796949429608364,
-1.4460169086618335,
-1.443747946983625,
-1.441473928002975,
-1.4391948404476311,
-1.4369106730202168,
-1.4346214143981741,
-1.4323270532337087,
... |
Given that following time series exhibit piecewise linear trend, how many pieces are there? | [
"4",
"2",
"1"
] | 1 | multiple_choice | null | null | 5 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Piecewise Linear Trend"
] | Check if the time series values increase or decrease linearly over time with different slopes. The slope change could be both positive and negative. | Pattern Recognition | Trend Recognition | 296 | [
0.0058303102952377865,
-0.013998578608483052,
0.011493697654284811,
-0.02166361145882146,
0.029355508705259763,
-0.011858513731929707,
-0.03204294402723076,
-0.00006195751639513679,
-0.0056305458050034726,
-0.014006853988716808,
0.024358057961260466,
0.014552870368286739,
0.00977656533117363... |
You are given two time series where one is the lagged version of the other. What is the most likely lagging step? | [
"Lagging step is between 60 to 75",
"Lagging step is between 5 to 10",
"Lagging step is between 30 to 45"
] | Lagging step is between 5 to 10 | multiple_choice | [
-0.0033982039402142505,
0.02076333526576436,
0.08677665148282614,
0.09214375350081082,
0.09565768423431631,
0.09574690894184919,
0.12019652745567698,
0.12522480057894836,
0.09923010968555743,
0.10004003657722631,
0.10945513964095335,
0.10982509276260839,
0.10838078245261916,
0.072330209756... | [
0.12019652745567698,
0.12522480057894836,
0.09923010968555743,
0.10004003657722631,
0.10945513964095335,
0.10982509276260839,
0.10838078245261916,
0.07233020975671547,
0.09469298674958498,
0.09419972344306804,
0.05764719242231646,
0.05153181311637969,
0.07202322041319081,
0.096298061557196... | 98 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Lagged Pair"
] | You already know that one time series is the lagged version of the other. Shift the time series by lags proposed in the options and check which one looks the same as the other time series. | Causality Analysis | Granger Causality | 297 | null |
Is the given time series likely to be stationary after removing the trend? | [
"No",
"Yes"
] | No | binary | null | null | 34 | easy | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Stationarity",
"Linear Trend",
"Exponential Trend"
] | Trend brings the overall shape of the time series up or down. Assume this effect is removed, does the time series satisfy the stationarity condition? | Pattern Recognition | Stationarity Detection | 298 | [
1.002118326712022,
1.1851687126057073,
1.6945876482108468,
1.6341387558715341,
1.901442854211147,
2.0483375184743737,
2.4515185813562166,
2.6574731143872468,
2.661043373549978,
2.8111407034677898,
2.6959355720595752,
2.8370030117423304,
2.758835578869663,
2.5823879988127505,
2.5061412983... |
The following time series has two types of anomalies appearing at different time points. What are the likely types of anomalies? | [
"Cutoff: the pattern of time series disappeared for certain point in time and became flat and Flip: the pattern is flipped at certain point in time",
"Speedup: the period of cyclic components is different from other parts of the time series and Flip: the pattern is flipped at certain point in time",
"Speedup: t... | Cutoff: the pattern of time series disappeared for certain point in time and became flat and Flip: the pattern is flipped at certain point in time | multiple_choice | null | null | 68 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Cutoff Anomaly",
"Flip Anomaly",
"Speed Up/Down Anomaly"
] | You should first identify the two places where the anomalies appear. Then, you should check the type of anomaly based on the given definitions. | Anolmaly Detection | General Anomaly Detection | 299 | [
0,
0.9274249992951756,
1.5122374644737526,
1.5391987068932718,
1.000463662982474,
0.0984974714693842,
-0.829195454346823,
-1.4355462143097473,
-1.4929520202756652,
-0.9778926052628722,
-0.07967440690591976,
0.8699483198687877,
1.5201105410262128,
1.631278339667621,
1.1643009882075297,
... |
The given timeseries is a combination of trend, seasonality and noise. Can you identify the pattern despite the noise? | [
"Noise dominated",
"Yes, Linear Trend and Sine Wave"
] | Yes, Linear Trend and Sine Wave | multiple_choice | null | null | 13 | hard | Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess. | [
"Linear Trend",
"Sine Wave",
"Gaussian White Noise"
] | Identify which component (trend, seasonality, or noise) has the largest impact on the overall pattern. | Pattern Recognition | Trend Recognition | 300 | [
0.5980318719039158,
0.4413978250203084,
1.4506832398990397,
1.2146957686769095,
3.0232367258561377,
3.23794207223758,
2.3380630507135867,
2.1315030323470086,
2.4043746835049826,
3.8268705227130986,
2.055697393393371,
0.8348176670649563,
0.8418451865071698,
0.22758679288386427,
0.66879543... |
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