Information related to a time series can be predicted. For example, a repetitive characteristic of the time series can be determined by analyzing the time series for a pattern that repeats over a predetermined time period. An adjusted time series can be generated by removing the repetitive characteristic from the time series. An effect of a moving event on the adjusted time series can be determined. The moving event can occur on different dates for two or more consecutive years. A residual time series can be generated by removing the effect of the moving event from the adjusted time series. A base forecast that is independent of the repetitive characteristic and the effect of the moving event can be generated using the residual time series. A predictive forecast can be generated by including the repetitive characteristic and the effect of the moving event into the base forecast.
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 time series comprising a plurality of data points arranged in a sequential order over a period of time; determine a repetitive characteristic of the time series by analyzing the time series for a pattern that repeats over a predetermined time period; generate an adjusted time series by removing the repetitive characteristic from the time series; determine, 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; generate a residual time series by removing the effect of the one or more moving events from the adjusted time series; generate, 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 generate a predictive forecast by including the repetitive characteristic and the effect of the one or more moving events into the base forecast. 2. The non-transitory computer readable medium of claim 1, further comprising program code executable by the processor for causing the processor to pre-process the time series prior to determining the repetitive characteristic for the time series by: determining that the time series is to be combined with other time series data based on a length of the time series being below a threshold; and generating an aggregate time-series by combining the time series with the other time series data. 3. The non-transitory computer readable medium of claim 2, further comprising program code executable by the processor for causing the processor to pre-process the time series prior to determining the repetitive characteristic for the time series by: smoothing the aggregate time-series to generate a smoothed aggregate time-series; and using the smoothed aggregate time-series as the time series. 4. The non-transitory computer readable medium of claim 3, further comprising program code executable by the processor for causing the processor to smooth the aggregate time-series by: removing data points associated with one or more predetermined events from the time series; determining replacement data points to be included in the time series in locations corresponding to the removed data points; and including the replacement data points in the time series in the locations corresponding to the removed data points. 5. The non-transitory computer readable medium of claim 1, further comprising program code executable by the processor for causing the processor to determine the effect of the one or more moving events by: generating a pooled time series from the time series and other time-series data using a clustering method or a hierarchical method; and determining the effect of the one or more moving events based on the pooled time series. 6. 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 is usable to generate the predictive forecast prior to determining the repetitive characteristic for the time series by: receiving a plurality of time series comprising the time series; and filtering the plurality of time series using a threshold time duration to identify a subset of time series from the plurality of time series in which the time durations exceed the threshold time duration, the subset of time series comprising the time series. 7. The non-transitory computer readable medium of claim 6, further comprising program code executable by the processor for causing the processor to: determine that the time series does not include a time period with inactivity; and determine that the time series exhibits the repetitive characteristic by analyzing the time series to detect a presence of the repetitive characteristic. 8. The non-transitory computer readable medium of claim 7, further comprising program code executable by the processor for causing the processor to determine that the time series comprises a magnitude spike with a value above a magnitude threshold. 9. The non-transitory computer readable medium of claim 8, further comprising program code executable by the processor for causing the processor to flag the time series as usable to generate the predictive forecast 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 magnitude threshold. 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 time series comprising a plurality of data points arranged in a sequential order over a period of time; determining a repetitive characteristic of the time series by analyzing the time series for a pattern that repeats over a predetermined time period; 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 a predictive forecast by including the repetitive characteristic and the effect of the one or more moving events into the base forecast. 12. The method of claim 11, further comprising pre-processing the time series prior to determining the repetitive characteristic for the time series by: determining that the time series is to be combined with other time series data based on a length of the time series being below a threshold; and generating an aggregate time-series by combining the time series with the other time series data. 13. The method of claim 12, further comprising pre-processing the time series prior to determining the repetitive characteristic for the time series by: smoothing the aggregate time-series to generate a smoothed aggregate time-series; and using the smoothed aggregate time-series as the time series. 14. The method of claim 13, further comprising smoothing the aggregate time-series by: removing data points associated with one or more predetermined events from the time series; determining replacement data points to be included in the time series in locations corresponding to the removed data points; and including the replacement data points in the time series in the locations corresponding to the removed data points. 15. The method of claim 11, further comprising determining the effect of the one or more moving events by: generating a pooled time series from the time series and other time-series data using a clustering method or a hierarchical method; and determining the effect of the one or more moving events based on the pooled time series. 16. The method of claim 11, further comprising determining that the time series is usable to generate the predictive forecast prior to determining the repetitive characteristic for the time series by: receiving a plurality of time series comprising the time series; and filter the plurality of time series using a threshold time duration to identify a subset of time series from the plurality of time series in which the time durations exceed the threshold time duration, the subset of time series comprising the time series. 17. The method of claim 16, further comprising: determining that the time series does not include a time period with inactivity; and determine that the time series exhibits the repetitive characteristic by analyzing the time series to detect a presence of the repetitive characteristic. 18. The method of claim 17, further comprising determining that the time series comprises a magnitude spike with a value above a magnitude threshold. 19. The method of claim 18, further comprising flagging the time series as usable to generate the predictive forecast 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 magnitude threshold. 20. The method of claim 11, wherein: determining the base forecast comprises a first worker node of a communications grid computing system receiving data from a second worker node of the communications grid computing system, generating the base forecast based on the data, and transmitting information associated with the base forecast to a third worker node of the communications grid computing system; and determining the predictive forecast comprises the third worker node of the communications grid computing system receiving the information associated with the base forecast and generating the predictive forecast based on the information. 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 time series comprising a plurality of data points arranged in a sequential order over a period of time; determine a repetitive characteristic of the time series by analyzing the time series for a pattern that repeats over a predetermined time period; generate an adjusted time series by removing the repetitive characteristic from the time series; determine, 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; generate a residual time series by removing the effect of the one or more moving events from the adjusted time series; generate, 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 generate a predictive forecast by including the repetitive characteristic and the effect of the one or more moving events into the base forecast. 22. The system of claim 21, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to pre-process the time series prior to determining the repetitive characteristic for the time series by: determining that the time series is to be combined with other time series data based on a length of the time series being below a threshold; and generating an aggregate time-series by combining the time series with the other time series data. 23. The system of claim 22, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to pre-process the time series prior to determining the repetitive characteristic for the time series by: smoothing the aggregate time-series to generate a smoothed aggregate time-series; and using the smoothed aggregate time-series as the time series. 24. The system of claim 23, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to smooth the aggregate time-series by: removing data points associated with one or more predetermined events from the time series; determining replacement data points to be included in the time series in locations corresponding to the removed data points; and including the replacement data points in the time series in the locations corresponding to the removed data points. 25. The system of claim 21, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to determine the effect of the one or more moving events by: generating a pooled time series from the time series and other time-series data using a clustering method or a hierarchical method; and determining the effect of the one or more moving events based on the pooled time series. 26. 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 is usable to generate the predictive forecast prior to determining the repetitive characteristic for the time series by: receiving a plurality of time series comprising the time series; and filter the plurality of time series using a threshold time duration to identify a subset of time series from the plurality of time series in which the time durations exceed the threshold time duration, the subset of time series comprising the time series. 27. The system of claim 26, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to: determine that the time series does not include a time period with inactivity; and determine that the time series exhibits the repetitive characteristic by analyzing the time series to detect a presence of the repetitive characteristic. 28. The system of claim 27, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to: determine that the time series comprises a magnitude spike with a value above a magnitude threshold. 29. The system of claim 28, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to flag the time series as usable to generate the predictive forecast 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 magnitude threshold. 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 base forecast and transmit information associated with the base forecast 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 information associated with the base forecast and generate the predictive forecast based on the information.