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; determine that a first time series in the plurality of time series is compatible with a three-stage forecasting process by: determining that the first time series has a duration that exceeds a minimum time duration usable with the three-stage forecasting process; determining that the first time series does not include a time period with inactivity; determining that the first time series exhibits a seasonal characteristic, the seasonal characteristic being a repetitive characteristic that repeats over a predetermined time period; determining that the first time series comprises a magnitude spike with a value above a preset magnitude threshold, the magnitude spike indicating a moving event that occurs on different days for at least two consecutive years; and in response to determining that the first time series (i) lacks the time period with inactivity, (ii) exhibits the seasonal characteristic, and (iii) comprises the magnitude spike with the value above the preset magnitude threshold, determining that the first time series is compatible with the three-stage forecasting process; generate a predictive forecast from the first time series using the three-stage forecasting process at least in part by: determining a time-series group for the first time series from a plurality of time-series groups by: determining an attribute of the first time series, the attribute comprising a frequency of events in the first time series, a timing of events in the first 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 first time series as input for a clustering method; and receiving the time-series group as output from the clustering method; generating a data set that includes the first time series in the time-series group; and generating the predictive forecast from the data set using the three-stage forecasting process, the predictive forecast indicating a progression of the first time series over a future period of time; determine that a second time series in the plurality of time series is incompatible with the three-stage forecasting process; and use another forecasting process that is different from the three-stage forecasting process to generate another predictive forecast from the second time series. 2. The non-transitory computer readable medium of claim 1, wherein the three-stage forecasting process comprises: determining the seasonal characteristic exhibited by the first time series; generating an adjusted time series by removing the seasonal characteristic from the first 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 seasonal characteristic and the effect of the one or more moving events; and generating the predictive forecast by including the seasonal 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 first time series comprises the magnitude spike with the value above the preset magnitude threshold by: removing the seasonal characteristic from the first time series to generate a base time series; determining one or more magnitude differences between the first 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 first 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 determine that the second time series is incompatible with the three-stage forecasting process by: determining that a time duration of the second time series is below the minimum time duration usable with the three-stage 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 seasonal characteristic; 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 minimum time duration usable with the three-stage forecasting process, (ii) the second time series comprises the time period with the inactivity, (iii) the second time series does not exhibit the seasonal 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 three-stage forecasting process. 5. The non-transitory computer readable medium of claim 1, further comprising program code executable by the processor for causing the processor to: prior to determining the first time series exhibits the seasonal characteristic, determine that a time duration of the first time series is above a first preset time duration and below a second preset time duration and, in response: aggregate the first time series with another time series to generate an aggregate time series; and use the aggregate time series as the first time series. 6. The non-transitory computer readable medium of claim 5, wherein the first preset time duration is one year and the second preset time duration is two years. 7. 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. 8. A computer-implemented 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; determining that a first time series in the plurality of time series is compatible with a three-stage forecasting process by: determining that the first time series has a duration that exceeds a minimum time duration usable with the three-stage forecasting process; determining that the first time series does not include a time period with inactivity; determining that the first time series exhibits a seasonal characteristic, the seasonal characteristic being a repetitive characteristic that repeats over a predetermined time period; determining that the first time series comprises a magnitude spike with a value above a preset magnitude threshold, the magnitude spike indicating a moving event that occurs on different days for at least two consecutive years; and in response to determining that the first time series (i) lacks the time period with inactivity, (ii) exhibits the seasonal characteristic, and (iii) comprises the magnitude spike with the value above the preset magnitude threshold, determining that the first time series is compatible with the three-stage forecasting process; generating a predictive forecast from the first time series using the three-stage forecasting process at least in part by: determining a time-series group for the first time series from a plurality of time-series groups by: determining an attribute of the first time series, the attribute comprising a frequency of events in the first time series, a timing of events in the first 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 first time series as input for a clustering method; and receiving the time-series group as output from the clustering method; generating a data set that includes the first time series in the time-series group; and generating the predictive forecast from the data set using the three-stage forecasting process, the predictive forecast indicating a progression of the first time series over a future period of time; determining that a second time series in the plurality of time series is incompatible with the three-stage forecasting process; and using another forecasting process that is different from the three-stage forecasting process to generate another predictive forecast from the second time series. 9. The computer-implemented method of claim 8, wherein the three-stage forecasting process comprises: determining the seasonal characteristic exhibited by the first time series; generating an adjusted time series by removing the seasonal characteristic from the first 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 seasonal characteristic and the effect of the one or more moving events; and generating the predictive forecast by including the seasonal characteristic and the effect of the one or more moving events into the base forecast. 10. The computer-implemented method of claim 8, further comprising determining that the first time series comprises the magnitude spike with the value above the preset magnitude threshold by: removing the seasonal characteristic from the first time series to generate a base time series; determining one or more magnitude differences between the first 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 first time series comprises the magnitude spike with the value above the preset magnitude threshold. 11. The computer-implemented method of claim 8, further comprising determining that the second time series is incompatible with the three-stage forecasting process by: determining that a time duration of the second time series is below the minimum time duration usable with the three-stage 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 seasonal characteristic; 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 minimum time duration usable with the three-stage forecasting process, (ii) the second time series comprises the time period with the inactivity, (iii) the second time series does not exhibit the seasonal 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 three-stage forecasting process. 12. The computer-implemented method of claim 8, further comprising: prior to determining the first time series exhibits the seasonal characteristic, determining that a time duration of the first time series is above a first preset time duration and below a second preset time duration and, in response: aggregating the first time series with another time series to generate an aggregate time series; and using the aggregate time series as the first time series. 13. The computer-implemented method of claim 12, wherein the first preset time duration is one year and the second preset time duration is two years. 14. The computer-implemented method of claim 8, 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. 15. 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; determine that a first time series in the plurality of time series is compatible with a three-stage forecasting process by: determining that the first time series has a duration that exceeds a minimum time duration usable with the three-stage forecasting process; determining that the first time series does not include a time period with inactivity; determining that the first time series exhibits a seasonal characteristic, the seasonal characteristic being a repetitive characteristic that repeats over a predetermined time period; determining that the first time series comprises a magnitude spike with a value above a preset magnitude threshold, the magnitude spike indicating a moving event that occurs on different days for at least two consecutive years; and in response to determining that the first time series (i) lacks the time period with inactivity, (ii) exhibits the seasonal characteristic, and (iii) comprises the magnitude spike with the value above the preset magnitude threshold, determining that the first time series is compatible with the three-stage forecasting process; generate a predictive forecast from the first time series using the three-stage forecasting process at least in part by: determining a time-series group for the first time series from a plurality of time-series groups by: determining an attribute of the first time series, the attribute comprising a frequency of events in the first time series, a timing of events in the first 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 first time series as input for a clustering method; and receiving the time-series group as output from the clustering method; generating a data set that includes the first time series in the time-series group; and generating the predictive forecast from the data set using the three-stage forecasting process, the predictive forecast indicating a progression of the first time series over a future period of time; determine that a second time series in the plurality of time series is incompatible with the three-stage forecasting process; and use another forecasting process that is different from the three-stage forecasting process to generate another predictive forecast from the second time series. 16. The system of claim 15, wherein the three-stage forecasting process comprises: determining the seasonal characteristic exhibited by the first time series; generating an adjusted time series by removing the seasonal characteristic from the first 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 seasonal characteristic and the effect of the one or more moving events; and generating the predictive forecast by including the seasonal characteristic and the effect of the one or more moving events into the base forecast. 17. The system of claim 15, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to: determine that the first time series comprises the magnitude spike with the value above the preset magnitude threshold by: removing the seasonal characteristic from the first time series to generate a base time series; determining one or more magnitude differences between the first 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 first time series comprises the magnitude spike with the value above the preset magnitude threshold. 18. The system of claim 15, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to determine that the second time series is incompatible with the three-stage forecasting process by: determining that a time duration of the second time series is below the minimum time duration usable with the three-stage 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 seasonal characteristic; 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 minimum time duration usable with the three-stage forecasting process, (ii) the second time series comprises the time period with the inactivity, (iii) the second time series does not exhibit the seasonal 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 three-stage forecasting process. 19. The system of claim 15, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to: prior to determining the first time series exhibits the seasonal characteristic, determine that a time duration of the first time series is above a first preset time duration and below a second preset time duration and, in response: aggregate the first time series with another time series to generate an aggregate time series; and use the aggregate time series as the first time series. 20. The system of claim 19, wherein the first preset time duration is one year and the second preset time duration is two years. 21. The system of claim 15, 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.