Data sets for a three-stage predictor can be automatically determined. For example, multiple time series can be filtered to identify a subset of time series that have time durations that exceed a preset time duration. Whether a time series of the subset of time series includes a time period with inactivity can be determined. Whether the time series exhibits a repetitive characteristic can be determined based on whether the time series has a pattern that repeats over a predetermined time period. Whether the time series includes a magnitude spike with a value above a preset magnitude can be determined. If the time series (i) lacks the time period with inactivity, (ii) exhibits the repetitive characteristic, and (iii) has the magnitude spike with the value above the preset magnitude threshold, the time series can be included in a data set for use with the three-stage predictor.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A non-transitory computer readable medium comprising program code executable by a processor for causing the processor to: receive a plurality of time series, each time series of the plurality of time series comprising a plurality of data points arranged in a sequential order over a period of time; filter the plurality of time series using a preset time duration to identify a subset of time series that have time durations that exceed the preset time duration, the preset time duration being a minimum time duration usable with a preselected forecasting process; and in response to identifying the subset of time series that exceeds the preset time duration: determine that a time series of the subset of time series does not include a time period with inactivity; determine that the time series exhibits a repetitive characteristic based on the time series comprising a pattern that repeats over a predetermined time period; determine that the time series comprises a magnitude spike with a value above a preset magnitude threshold; and in response to determining that the time series (i) lacks the time period with inactivity, (ii) exhibits the repetitive characteristic, and (iii) comprises the magnitude spike with the value above the preset magnitude threshold: generate a data set that includes the time series; and generate a predictive forecast from the data set using the preselected forecasting process, the predictive forecast indicating a progression of the time series over a future period of time. 2. The non-transitory computer readable medium of claim 1, wherein the preselected forecasting process comprises: determining the repetitive characteristic exhibited by the time series; generating an adjusted time series by removing the repetitive characteristic from the time series; determining, using the adjusted time series, an effect of one or more moving events that occur on different dates for two or more consecutive years on the adjusted time series; generating a residual time series by removing the effect of the one or more moving events from the adjusted time series; generating, using the residual time series, a base forecast that is independent of the repetitive characteristic and the effect of the one or more moving events; and generating the predictive forecast by including the repetitive characteristic and the effect of the one or more moving events into the base forecast. 3. The non-transitory computer readable medium of claim 1, further comprising program code executable by the processor for causing the processor to determine that the time series comprises the magnitude spike with the value above the preset magnitude threshold by: removing the repetitive characteristic from the time series to generate a base time series; determining one or more magnitude differences between the time series and the base time series; determining that the one or more magnitude differences exceed the preset magnitude threshold; and in response to determining that the one or more magnitude differences exceed the preset magnitude threshold, determining that the time series comprises the magnitude spike with the value above the preset magnitude threshold. 4. The non-transitory computer readable medium of claim 1, further comprising program code executable by the processor for causing the processor to generate the data set that includes the time series by: determining a time-series group for the time series from a plurality of time-series groups using a clustering method; and including the time series in the time-series group, the time-series group being the data set. 5. The non-transitory computer readable medium of claim 4, further comprising program code executable by the processor for causing the processor to: determine the time-series group for the time series from the plurality of time-series groups using the clustering method by: determining an attribute of the time series comprising a frequency of events in the time series, a timing of events in the time series, an average percentage of lift with respect to a base time series, or a maximum percentage of lift with respect to the base time series; using the attribute of the time series as input for the clustering method; and receiving the time-series group as output from the clustering method. 6. The non-transitory computer readable medium of claim 1, wherein the time series is a first time series, and further comprising program code executable by the processor for causing the processor to: determine that a time duration of a second time series of the plurality of time series is below the preset time duration usable with the preselected forecasting process; or determine that the second time series comprises the time period with the inactivity; or determine that the second time series does not exhibit the repetitive characteristic based on an absence of the event; or determine that the second time series does not comprise the magnitude spike with the value above the preset magnitude threshold; and in response to determining that (i) the time duration of the second time series is below the preset time duration, (ii) the second time series comprises the time period with the inactivity, (iii) the second time series does not exhibit the repetitive characteristic, or (iii) the second time series does not comprise the magnitude spike with the value above the preset magnitude threshold, flag the second time series as incompatible with the preselected forecasting process. 7. The non-transitory computer readable medium of claim 6, further comprising program code executable by the processor for causing the processor to: select another forecasting process for use with the second time series; and use the other forecasting process to generate another forecast from the second time series. 8. The non-transitory computer readable medium of claim 1, wherein the preset time duration usable with the preselected forecasting process is a first preset time duration, and further comprising program code executable by the processor for causing the processor to: prior to determining the time series exhibits the repetitive characteristic, determine that a time duration of the time series is above the first preset time duration and below a second preset time duration and, in response: aggregate the time series with another time series to generate an aggregate time series; and use the aggregate time series as the time series. 9. The non-transitory computer readable medium of claim 8, wherein the first preset time duration is one year and the second preset time duration is two years. 10. The non-transitory computer readable medium of claim 1, wherein the non-transitory computer readable medium comprises two or more computer readable media distributed among two or more worker nodes in a communications grid computing system, the two or more worker nodes being separate computing devices that are remote from one another. 11. A method comprising: receiving a plurality of time series, each time series of the plurality of time series comprising a plurality of data points arranged in a sequential order over a period of time; filtering the plurality of time series using a preset time duration to identify a subset of time series that have time durations that exceed the preset time duration, the preset time duration being a minimum time duration usable with a preselected forecasting process; and in response to identifying the subset of time series that exceeds the preset time duration: determining that a time series of the subset of time series does not include a time period with inactivity; determining that the time series exhibits a repetitive characteristic based on the time series comprising a pattern that repeats over a predetermined time period; determining that the time series comprises a magnitude spike with a value above a preset magnitude threshold; and in response to determining that the time series (i) lacks the time period with inactivity, (ii) exhibits the repetitive characteristic, and (iii) comprises the magnitude spike with the value above the preset magnitude threshold: generating a data set that includes the time series; and generating a predictive forecast from the data set using the preselected forecasting process, the predictive forecast indicating a progression of the time series over a future period of time. 12. The method of claim 11, wherein the preselected forecasting process comprises: determining the repetitive characteristic exhibited by the time series; generating an adjusted time series by removing the repetitive characteristic from the time series; determining, using the adjusted time series, an effect of one or more moving events that occur on different dates for two or more consecutive years on the adjusted time series; generating a residual time series by removing the effect of the one or more moving events from the adjusted time series; generating, using the residual time series, a base forecast that is independent of the repetitive characteristic and the effect of the one or more moving events; and generating the predictive forecast by including the repetitive characteristic and the effect of the one or more moving events into the base forecast. 13. The method of claim 11, further comprising determining that the time series comprises the magnitude spike with the value above the preset magnitude threshold by: removing the repetitive characteristic from the time series to generate a base time series; determining one or more magnitude differences between the time series and the base time series; determining that the one or more magnitude differences exceed the preset magnitude threshold; and in response to determining that the one or more magnitude differences exceed the preset magnitude threshold, determining that the time series comprises the magnitude spike with the value above the preset magnitude threshold. 14. The method of claim 11, further comprising generating the data set that includes the time series by: determining a time-series group for the time series from a plurality of time-series groups using a clustering method; and including the time series in the time-series group, the time-series group being the data set. 15. The method of claim 14, further comprising determining the time-series group for the time series from the plurality of time-series groups using the clustering method by: determining an attribute of the time series comprising a frequency of events in the time series, a timing of events in the time series, an average percentage of lift with respect to a base time series, or a maximum percentage of lift with respect to the base time series; using the attribute of the time series as input for the clustering method; and receiving the time-series group as output from the clustering method. 16. The method of claim 11, wherein the time series is a first time series, and further comprising: determining that a time duration of a second time series of the plurality of time series is below the preset time duration usable with the preselected forecasting process; or determining that the second time series comprises the time period with the inactivity; or determining that the second time series does not exhibit the repetitive characteristic based on an absence of the event; or determining that the second time series does not comprise the magnitude spike with the value above the preset magnitude threshold; and in response to determining that (i) the time duration of the second time series is below the preset time duration, (ii) the second time series comprises the time period with the inactivity, (iii) the second time series does not exhibit the repetitive characteristic, or (iii) the second time series does not comprise the magnitude spike with the value above the preset magnitude threshold, flagging the second time series as incompatible with the preselected forecasting process. 17. The method of claim 16, further comprising: selecting another forecasting process for use with the second time series; and using the other forecasting process to generate another forecast from the second time series. 18. The method of claim 11, wherein the preset time duration usable with the preselected forecasting process is a first preset time duration, and further comprising prior to determining the time series exhibits the repetitive characteristic, determining that a time duration of the time series is above the first preset time duration and below a second preset time duration and, in response: aggregating the time series with another time series to generate an aggregate time series; and using the aggregate time series as the time series. 19. The method of claim 18, wherein the first preset time duration is one year and the second preset time duration is two years. 20. The method of claim 11, wherein: generating the data set comprises a first worker node of a communications grid computing system receiving information from a second worker node of the communications grid computing system, generating the data set based on the information, and transmitting the data set to a third worker node of the communications grid computing system; and generating the predictive forecast comprises the third worker node of the communications grid computing system receiving the data set and generating the predictive forecast based on the data set. 21. A system comprising: a processing device; and a memory device in which instructions executable by the processing device are stored for causing the processing device to: receive a plurality of time series, each time series of the plurality of time series comprising a plurality of data points arranged in a sequential order over a period of time; filter the plurality of time series using a preset time duration to identify a subset of time series that have time durations that exceed the preset time duration, the preset time duration being a minimum length usable with a preselected forecasting process; and in response to identifying the subset of time series that exceeds the preset time duration: determine that a time series of the subset of time series does not include a time period with inactivity; determine that the time series exhibits a repetitive characteristic based on the time series comprising a pattern that repeats over a predetermined time period; determine that the time series comprises a magnitude spike with a value above a preset magnitude threshold; and in response to determining that the time series (i) lacks the time period with inactivity, (ii) exhibits the repetitive characteristic, and (iii) comprises the magnitude spike with the value above the preset magnitude threshold: generate a data set that includes the time series; and generate a predictive forecast from the data set using the preselected forecasting process, the predictive forecast indicating a progression of the time series over a future period of time. 22. The system of claim 21, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to generate the predictive forecast by: determining the repetitive characteristic exhibited by the time series; generating an adjusted time series by removing the repetitive characteristic from the time series; determining, using the adjusted time series, an effect of one or more moving events that occur on different dates for two or more consecutive years on the adjusted time series; generating a residual time series by removing the effect of the one or more moving events from the adjusted time series; generating, using the residual time series, a base forecast that is independent of the repetitive characteristic and the effect of the one or more moving events; and generating the predictive forecast by including the repetitive characteristic and the effect of the one or more moving events into the base forecast. 23. The system of claim 21, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to: determine that the time series comprises the magnitude spike with the value above the preset magnitude threshold by: removing the repetitive characteristic from the time series to generate a base time series; determining one or more magnitude differences between the time series and the base time series; determining that the one or more magnitude differences exceed the preset magnitude threshold; and in response to determining that the one or more magnitude differences exceed the preset magnitude threshold, determining that the time series comprises the magnitude spike with the value above the preset magnitude threshold. 24. The system of claim 21, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to generate the data set that includes the time series by: determining a time-series group for the time series from a plurality of time-series groups using a clustering method; and including the time series in the time-series group, the time-series group being the data set. 25. The system of claim 24, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to: determine the time-series group for the time series from the plurality of time-series groups using the clustering method by: determining an attribute of the time series comprising a frequency of events in the time series, a timing of events in the time series, an average percentage of lift with respect to a base time series, or a maximum percentage of lift with respect to the base time series; using the attribute of the time series as input for the clustering method; and receiving the time-series group as output from the clustering method. 26. The system of claim 21, wherein the time series is a first time series, and wherein the memory device further comprises instructions executable by the processing device for causing the processing device to: determine that a time duration of a second time series of the plurality of time series is below the preset time duration usable with the preselected forecasting process; or determine that the second time series comprises the time period with the inactivity; or determine that the second time series does not exhibit the repetitive characteristic based on an absence of the event; or determine that the second time series does not comprise the magnitude spike with the value above the preset magnitude threshold; and in response to determining that (i) the time duration of the second time series is below the preset time duration, (ii) the second time series comprises the time period with the inactivity, (iii) the second time series does not exhibit the repetitive characteristic, or (iii) the second time series does not comprise the magnitude spike with the value above the preset magnitude threshold, flag the second time series as incompatible with the preselected forecasting process. 27. The system of claim 26, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to: select another forecasting process for use with the second time series; and use the other forecasting process to generate another forecast from the second time series. 28. The system of claim 21, wherein the preset time duration usable with the preselected forecasting process is a first preset time duration, and wherein the memory device further comprises instructions executable by the processing device for causing the processing device to: prior to determining the time series exhibits the repetitive characteristic, determine that a time duration of the time series is above the first preset time duration and below a second preset time duration and, in response: aggregate the time series with another time series to generate an aggregate time series; and use the aggregate time series as the time series. 29. The system of claim 28, wherein the first preset time duration is one year and the second preset time duration is two years. 30. The system of claim 21, further comprising a plurality worker nodes in a communications grid computing system, wherein: a first worker node of the plurality of worker nodes is configured to generate the data set and transmit the data set to a second worker node of the plurality of worker nodes; and the second worker node of the plurality of worker nodes is configured to receive the data set and generate the predictive forecast based on the data set.