A method and system of data analytics provide for retrieving data from a plurality of different sources, normalizing the data, predicting one or more values based on the normalized data, analyzing the normalized data based on the prediction, and delivering an output to a user based on the analysis. One such method includes using a computer to extract point observation data from different electronic data sources; converting time-series data into a plurality of reference (time, value) pairs; normalizing the reference (time, value) pairs using a coherence operation to convert the data into interval observation data; performing estimated weighted moving average with a residual bands adjustment to provide a range of predicted values; comparing current (time, value) pairs to the predicted value to identify anomalies.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A data analytics method comprising: at a computer comprising one or more processors and a non-transitory memory for storing programs to be executed by the processors: extracting a selection of point observation data from each of multiple different electronic data sources; identifying time-series data within the point observation data and converting the time-series data into a plurality of reference (time, value) pairs; normalizing the reference (time, value) pairs using a coherence operation to convert the point observation data into interval observation data; storing the reference (time, value) pairs on a non-transitory computer-readable medium as a compilation of reference (time, value) pairs; processing the compilation of reference (time, value) pairs with a prediction algorithm to provide a predicted value for current (time, value) pairs; obtaining current (time, value) pairs from one or more of the multiple and different electronic data sources; and comparing one or more of the current (time, value) pairs to the predicted value to determine if the current (time, value) pair is an anomaly. 2. The method of claim 1 comprising calculating a compound metric from the time-series data. 3. The method of claim 1, wherein the (time, value) pairs are normalized so that they are on the same time scale. 4. The method of claim 1, wherein the multiple different electronic data sources include websites, web services, internal databases, and/or static .csv files. 5. The method of claim 1, wherein the multiple different electronic data sources includes websites and web services. 6. The method of claim 1, wherein the electronic data includes numeric and non-numeric data. 7. The method of claim 1, wherein the numeric (time, value) pairs are normalized according to the following steps: convert point observation (time-value) pairs that correspond to a point in time to interval observation (time, value) pairs that correspond to an interval of time that takes place over the interval [t, t′], where t is time of occurrence for a (time, value) pair and t′ is a time of occurrence for a (time, value) pair occurring next in time, and t′ is set as current time for time of occurrence of a last (time, value) pair in a set of (time, value) pairs being normalized; and construct a normalized (time, value) pair for a period for each set of interval observation (time, value) pairs whose intervals overlap with that period, by weighting their values according to how much of the period they occupy. 8. The method of claim 1, wherein the electronic data is non-numeric and is normalized according to the following steps: determine pertinent metric-specific information, comprising keeping a portion or all of the data related to a (time, value) pair and throwing away the remainder; normalize each piece of data retained individually; and apply any normalization needed at a metric level. 9. The method of claim 1, wherein observations are normalized according to the following steps: within a set of observations, convert a point observation into an interval observation that takes place over an interval [t, t′], where t is time of occurrence of the point observation and t′ is time of occurrence of a next point observation in the set; for a last point observation of the set, equate t′ to be current time; and weigh values of each interval observation whose interval overlaps with the interval [t, t′] according to how much of the interval [t, t′] it occupies; wherein mean weighted value of each interval observation becomes a normalized observation's value with a time interval of [t, t′]. 10. The method of claim 1, wherein the prediction algorithm accepts a sequence of (time index, value) pairs as input, accepts a number of sequential predictions to make, and returns the following sequence as output: time index, predicted value, predicted low, predicted high wherein: predicted value is a most likely value; predicted low is a lowest value predicted to occur; and predicted high is a highest value predicted to occur. 11. The method of claim 1, wherein the prediction algorithm is configured to provide a predicted value for current (time, value) pairs based on analyzing averages of the data represented by the reference (time, value) pairs by accounting for qualitative changes in the data and the rate at which the averages of the data change over time. 12. The method of claim 1, wherein the prediction algorithm is an autoregressive integrated moving average (ARIMA) algorithm. 13. The method of claim 12, wherein the (ARIMA) algorithm uses any group containing from 2-14 (AR, I, MA) combinations selected from [0, 0, 0], [1, 0, 0], [1, 1, 0], [1, 1, 1], [0, 0, 1], [0, 1, 1], [0, 1, 0], [1, 0, 1], [2, 0, 0], [0, 0, 2], [2, 0, 2], [2, 1, 1], [1, 1, 2], and [2, 1, 2]. 14. The method of claim 1, wherein an anomaly is identified if the current (time, value) pair falls above or below a range defined by the predicted low and high. 15. The method of claim 1, wherein an alert selected from the group consisting of email, SMS, voice message, and mobile notification is sent to report whether or not an anomaly is found. 16. The method of claim 1, wherein the time-series data is selected from the group consisting of revenue, file download views, successful sign-ins, returning customer count, product registrations, click-throughs, bounce rate, referrals, impressions, visitors, visits, page views, and conversions.