A method, system, and computer program product for weight adjusted composite model for forecasting in anomalous environments are provided in the illustrative embodiments. A base forecasting model and a second forecasting model are combined to form a composite model, the base forecasting model configured to forecast an event in a time series, the second forecasting model configured to represent an anomalous portion of data in the time series. A mixing algorithm is combined with the composite model to adjust a set of weights associated with the composite model. Upon identifying a future period in which the event is to be forecasted, using the mixing algorithm, a subset of the set of weights is adjusted to from a weight adjusted composite model. The weight adjusted composite model is executed to forecast the event in the future period.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for event forecasting in anomalous environments, the method comprising: combining with a base forecasting model a second forecasting model to form a composite model, the base forecasting model configured to forecast an event in a time series, the second forecasting model configured to represent an anomalous portion of data in the time series; combining with the composite model a mixing algorithm, the mixing algorithm configured to adjust a set of weights associated with the composite model; adjusting, responsive to identifying a future period in which the event is to be forecasted, using the mixing algorithm, a subset of the set of weights to from a weight adjusted composite model; and executing, using a processor and a memory, the weight adjusted composite model to forecast the event in the future period. 2. The method of claim 1, further comprising: identifying a characteristic of the future period; identifying a second period in a training data, wherein the characteristic of the future period matches a corresponding characteristic of the second period within a threshold; and determining a portion of a curve representing a set of anomalous values in the training data during the second period, wherein the adjusting matches a curve representing the composite model during the future period with the portion of the curve representing the set of anomalous values in the training data during the second period. 3. The method of claim 2, wherein the training data comprises past data of the time series. 4. The method of claim 2, wherein the characteristic comprises a factor that affects the event during the future period. 5. The method of claim 2, wherein the characteristic comprises a time of day. 6. The method of claim 1, wherein the second forecasting model comprises an equation whose curve fits, within a threshold, a curve formed by a set of values in the anomalous portion, and wherein the base forecasting model forecasts a value of the event in a non-anomalous portion of the data of the time series. 7. The method of claim 1, further comprising: configuring the set of weights in the composite model, wherein a weight in the set of weights is associated with an expression in an equation in the composite model, wherein adjusting the weight causes a curve of the equation to change and form a changed curve, and wherein the changed fits, within a threshold, a second curve formed by a set of values in an expected anomalous portion in the time series during the future period. 8. The method of claim 1, wherein the second forecasting model comprises an equation representing a seasonal change in the time series. 9. The method of claim 1, wherein the second forecasting model comprises an equation representing a level change in the time series. 10. The method of claim 1, wherein the second forecasting model comprises an equation representing a trend change in the time series. 11. The method of claim 1, wherein the method is embodied in a computer program product comprising one or more computer-readable tangible storage devices and computer-readable program instructions which are stored on the one or more computer-readable tangible storage devices and executed by one or more processors. 12. The method of claim 1, wherein the method is embodied in a computer system comprising one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage devices and program instructions which are stored on the one or more computer-readable tangible storage devices for execution by the one or more processors via the one or more memories and executed by the one or more processors. 13. A computer program product for event forecasting in anomalous environments, the computer program product comprising: one or more computer-readable tangible storage devices; program instructions, stored on at least one of the one or more storage devices, to combine with a base forecasting model a second forecasting model to form a composite model, the base forecasting model configured to forecast an event in a time series, the second forecasting model configured to represent an anomalous portion of data in the time series; program instructions, stored on at least one of the one or more storage devices, to combine with the composite model a mixing algorithm, the mixing algorithm configured to adjust a set of weights associated with the composite model; program instructions, stored on at least one of the one or more storage devices, to adjust, responsive to identifying a future period in which the event is to be forecasted, using the mixing algorithm, a subset of the set of weights to from a weight adjusted composite model; and program instructions, stored on at least one of the one or more storage devices, to execute, using a processor and a memory, the weight adjusted composite model to forecast the event in the future period. 14. The computer program product of claim 13, further comprising: program instructions, stored on at least one of the one or more storage devices, to identify a characteristic of the future period; program instructions, stored on at least one of the one or more storage devices, to identify a second period in a training data, wherein the characteristic of the future period matches a corresponding characteristic of the second period within a threshold; and program instructions, stored on at least one of the one or more storage devices, to determine a portion of a curve representing a set of anomalous values in the training data during the second period, wherein the adjusting matches a curve representing the composite model during the future period with the portion of the curve representing the set of anomalous values in the training data during the second period. 15. The computer program product of claim 14, wherein the training data comprises past data of the time series. 16. The computer program product of claim 14, wherein the characteristic comprises a factor that affects the event during the future period. 17. The computer program product of claim 14, wherein the characteristic comprises a time of day. 18. The computer program product of claim 13, wherein the second forecasting model comprises an equation whose curve fits, within a threshold, a curve formed by a set of values in the anomalous portion, and wherein the base forecasting model forecasts a value of the event in a non-anomalous portion of the data of the time series. 19. The computer program product of claim 13, further comprising: program instructions, stored on at least one of the one or more storage devices, to configure the set of weights in the composite model, wherein a weight in the set of weights is associated with an expression in an equation in the composite model, wherein adjusting the weight causes a curve of the equation to change and form a changed curve, and wherein the changed fits, within a threshold, a second curve formed by a set of values in an expected anomalous portion in the time series during the future period. 20. A computer system for event forecasting in anomalous environments, the computer system comprising: one or more processors, one or more computer-readable memories and one or more computer-readable storage devices; program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to combine with a base forecasting model a second forecasting model to form a composite model, the base forecasting model configured to forecast an event in a time series, the second forecasting model configured to represent an anomalous portion of data in the time series; program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to combine with the composite model a mixing algorithm, the mixing algorithm configured to adjust a set of weights associated with the composite model; program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to adjust, responsive to identifying a future period in which the event is to be forecasted, using the mixing algorithm, a subset of the set of weights to from a weight adjusted composite model; and program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to execute, using a processor and a memory, the weight adjusted composite model to forecast the event in the future period.