In various implementations, a method includes receiving a set of time series data that corresponds to a metric. A seasonal pattern is extracted from the set of time series data and the extracted seasonal pattern is filtered from the set of time series data. A predictive model is generated from the filtered set of data. The extracted seasonal pattern is filtered from another set of time series data where the second set of time series data corresponds to the metric. The filtered second set of time series data is compared to the predictive model. An alert is generated to a user for a value within the filtered second set of time series data which falls outside of the predictive model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented system comprising: at least one processor; and at least one computer-readable media having a plurality of executable instructions embodied thereon, which, when executed by the at least one processor causes the at least one processor to perform a method comprising: extracting a seasonal pattern from a first set of time series data, the first set of time series data corresponding to a metric; filtering the extracted seasonal pattern from the first set of time series data; generating a predictive model from the filtered first set of time series data; filtering the extracted seasonal pattern from a second set of time series data, the second set of time series data corresponding to the metric; comparing the filtered second set of time series data to the predictive model; and providing an alert to a user based on the comparing indicating the filtered second set of time series data deviates from the predictive model. 2. The computer-implemented system of claim 1, wherein the filtering of the extracted seasonal pattern from the first set of time series data comprises applying a seasonality filter to the first set of time series data and the filtering the extracted seasonal pattern from the second set of time series data comprises applying the seasonality filter to the second set of time series data. 3. The computer-implemented system of claim 1, further comprising generating a set of expected ranges of the metric from the predictive model, wherein the comparing is of the filtered second set of time series data to the set of expected ranges. 4. The computer-implemented system of claim 1, wherein the extracting the seasonal pattern comprises: identifying a first frequency and a second frequency from a spectral density estimation of the first set of time series data; determining an amplitude of the first frequency is larger than tan amplitude an amplitude of the second frequency; and assigning the seasonal pattern to correspond to the first frequency based on the determining. 5. The computer-implemented system of claim 1, wherein the extracting the seasonal pattern comprises: identifying a first frequency and a second frequency from a spectral density estimation of the first set of time series data; determining the second frequency corresponds to a frequency leakage from the first frequency; based on the determining combining an amplitude of the first frequency with an amplitude of the second frequency resulting in a combined amplitude; assigning the combined amplitude to the first frequency; and assigning the seasonal pattern as the first frequency based on the combined amplitude; wherein the filtering the extracted seasonal pattern from the first set of time series data comprises reducing the combined amplitude in the first set of time series data using a seasonality filter tuned to the first frequency. 6. The computer-implemented system of claim 1, wherein the extracting the seasonal pattern comprises: identifying a first frequency from a spectral density estimation of the first set of time series data; comparing the first frequency to a threshold frequency; and assigning the seasonal pattern to correspond to the first frequency based on the comparing indicating the first frequency exceeds the threshold frequency. 7. The computer-implemented system of claim 1, wherein the alert comprises an electronic communication and the providing is of the electronic communication to a user device. 8. The computer-implemented system of claim 1, wherein the alert comprises an email and the providing comprises sending the email to an email address associated with the user. 9. A computer-implemented method comprising: extracting a first seasonal pattern from a first set of time series data, the first set of time series data corresponding to a metric; filtering the extracted first seasonal pattern from the first set of time series data; determining parameters of a predictive model based on the filtered first set of time series data, the parameters defining predicted future values of the metric as a function of time; filtering the extracted first seasonal pattern from a second set of time series data, the second set of time series data corresponding to the metric; generating at least one of the predicted future values from the predictive model based on a future time that corresponds to the filtered second set of time series data; identifying a deviation between the at least one of the predicted future values and the filtered second set of time series data; and providing an alert to a user device based on the identified deviation. 10. The computer-implemented method of claim 9, wherein the determining parameters of the predictive model comprises: updating prior parameters of the existing predictive model based on the filtered first set of time series data. 11. The computer-implemented method of claim 9, further comprising: determining a portion of the filtered first set of time series data conforms to the predicted future values; and based on the determining updating the parameters of the predictive model based on the portion of the filtered first set of time series data. 12. The computer-implemented method of claim 9, wherein the parameters of the predictive model further define expected ranges for the predicted future values of the filtered first set of time series data as a function of time, and wherein the identifying the deviation between the at least one of the predicted future values and the filtered second set of time series data comprises: determining that at least one value of the filtered second set of time series data falls outside of the expected range, the at least one value corresponding to the future time. 13. The computer-implemented method of claim 9, further comprising: determining a portion of the filtered time series the corresponds to the identified deviations; replacing the at least one of the value of the portion of the filtered time series with at least one value that is within an expected range of the predicted future values; and updating the parameters of the predictive model based on the corrected portion of the filtered time series. 14. The computer-implemented method of claim 9, further comprising: identifying a second seasonal pattern in the first set of time series data; using the parameters of the predictive model to define a predicted seasonal pattern in the predicted future values of the metric as a function of time; setting the predicted seasonal pattern based on the second seasonal pattern; and generating at least one of the predicted future values from the predictive model based on the predicted seasonal pattern. 15. The computer-implemented method of claim 9, wherein the predicted future values of the metric are defined in the predictive model using time intervals. 16. The computer-implemented system of claim 9, wherein the alert comprises a push notification and the providing is of the push notification for display on the user device. 17. At least one computer-readable media having a plurality of executable instructions embodied thereon, which, when executed by one or more processors, cause the one or more processors to perform a method comprising: receiving a first sequence of time series data in a stream of the time series data that is associated with a user; identifying a seasonal pattern in the first sequence of time series data; filtering the identified seasonal pattern from the first sequence of time series data; generating a predictive model from the filtered first sequence of the time series data; receiving a second sequence of the time series data that follows the first sequence of the time series data in the stream; detecting that a value in the second sequence the time series data falls outside of an expected range of the value predicted by the predictive model; and transmitting an alert to the user based on the detecting of the value. 18. The computer-readable media of claim 17, wherein the filtering the seasonal pattern comprises: tuning a seasonality filter to the seasonal pattern in the first sequence of time series data; and applying the tuned seasonality filter to the first sequence of time series data. 19. The computer-readable media of claim 17, wherein the stream of the time series data comprises at least one metric corresponding to user interaction with a web-site. 20. The computer-readable media of claim 17, further comprising updating the predictive model based on the filtered second sequence of time series data.