An input time-series is decomposed into a set of constituent frequencies. For each constituent frequency in a subset of the set of constituent frequencies, a corresponding forecasting model is selected in a subset from a set of forecasting models. From a set of component forecasts produced by the subset of forecasting models, a subset of component forecasts is selected. A component forecast in the subset of component forecasts is selected according to a component forecast selection condition. The subset of component forecasts is output to revise the forecast selection condition. A revised forecast selection condition increases a relevance of a future subset of component forecasts.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for forecasting based on time-series decomposition, the method comprising: decomposing, using a processor and a memory, an input time-series into a set of constituent frequencies; selecting, for each constituent frequency in a subset of the set of constituent frequencies, a corresponding forecasting model in a subset from a set of forecasting models; selecting, from a set of component forecasts produced by the subset of forecasting models, a subset of component forecasts, wherein a component forecast in the subset of component forecasts is selected according to a component forecast selection condition; and outputting the subset of component forecasts to revise the forecast selection condition, wherein a revised forecast selection condition increases a relevance of a future subset of component forecasts. 2. The method of claim 1, further comprising: receiving from an application a relevance weight, wherein the relevance weight is the component forecast selection condition. 3. The method of claim 2, wherein a user revises a previous relevance weight to form the relevance weight. 4. The method of claim 1, further comprising: receiving from an application an accuracy weight, wherein the accuracy weight is the component forecast selection condition. 5. The method of claim 4, wherein a user revises a previous accuracy weight to form the accuracy weight. 6. The method of claim 1, further comprising: receiving from an application a relevance weight and an accuracy weight, wherein the component forecast selection condition is a function of the relevance weight and the accuracy weight. 7. The method of claim 1, further comprising: producing, by providing a constituent frequency from the subset of constituent frequencies as an input to a corresponding forecasting model in the subset of forecasting models, a component forecast in the set of component forecasts. 8. The method of claim 1, further comprising: selecting the subset of the constituent frequencies according to a selection condition, wherein the selection condition specifies a minimum amplitude a frequency has to have in order to be selected into the subset of the constituent frequencies. 9. The method of claim 1, further comprising: selecting, for a first constituent frequency in the subset of the constituent frequencies, a first forecasting model that is configured to forecast when an anomaly is present in the first constituent frequency. 10. The method of claim 1, further comprising: transforming, as a part of decomposing, using Fourier transform, the input time-series, to detect a presence of a particular constituent frequency in the input time-series; and adding, responsive to the particular frequency being present in the input time-series, the particular frequency as a constituent frequency in the set of constituent frequencies. 11. The method of claim 10, further comprising: detecting, as a part of the transforming, the presence of the frequency at an amplitude. 12. The method of claim 1, wherein the decomposing uses a waveform representation of the input time-series, and wherein the decomposing produces a set of component data of the input time-series such that the set of the component data can be represented as the set of constituent frequencies. 13. The method of claim 1, wherein the method is embodied in a computer program product comprising one or more computer-readable 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. 14. 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 storage devices and program instructions which are stored on the one or more computer-readable storage devices for execution by the one or more processors via the one or more memories and executed by the one or more processors. 15. A computer program product for forecasting based on time-series decomposition, 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 decompose, using a processor and a memory, an input time-series into a set of constituent frequencies; program instructions, stored on at least one of the one or more storage devices, to select, for each constituent frequency in a subset of the set of constituent frequencies, a corresponding forecasting model in a subset from a set of forecasting models; program instructions, stored on at least one of the one or more storage devices, to select, from a set of component forecasts produced by the subset of forecasting models, a subset of component forecasts, wherein a component forecast in the subset of component forecasts is selected according to a component forecast selection condition; and program instructions, stored on at least one of the one or more storage devices, to output the subset of component forecasts to revise the forecast selection condition, wherein a revised forecast selection condition increases a relevance of a future subset of component forecasts. 16. The computer program product of claim 15, further comprising: program instructions, stored on at least one of the one or more storage devices, to receive from an application a relevance weight, wherein the relevance weight is the component forecast selection condition. 17. The computer program product of claim 16, wherein a user revises a previous relevance weight to form the relevance weight. 18. The computer program product of claim 15, further comprising: program instructions, stored on at least one of the one or more storage devices, to receive from an application an accuracy weight, wherein the accuracy weight is the component forecast selection condition. 19. The computer program product of claim 18, wherein a user revises a previous accuracy weight to form the accuracy weight. 20. The computer program product of claim 15, further comprising: program instructions, stored on at least one of the one or more storage devices, to receive from an application a relevance weight and an accuracy weight, wherein the component forecast selection condition is a function of the relevance weight and the accuracy weight. 21. The computer program product of claim 15, further comprising: program instructions, stored on at least one of the one or more storage devices, to produce, by providing a constituent frequency from the subset of constituent frequencies as an input to a corresponding forecasting model in the subset of forecasting models, a component forecast in the set of component forecasts. 22. The computer program product of claim 15, further comprising: program instructions, stored on at least one of the one or more storage devices, to select the subset of the constituent frequencies according to a selection condition, wherein the selection condition specifies a minimum amplitude a frequency has to have in order to be selected into the subset of the constituent frequencies. 23. A computer system for forecasting based on time-series decomposition, 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 decompose, using a processor and a memory, an input time-series into a set of constituent frequencies; 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 select, for each constituent frequency in a subset of the set of constituent frequencies, a corresponding forecasting model in a subset from a set of forecasting models; 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 select, from a set of component forecasts produced by the subset of forecasting models, a subset of component forecasts, wherein a component forecast in the subset of component forecasts is selected according to a component forecast selection condition; 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 output the subset of component forecasts to revise the forecast selection condition, wherein a revised forecast selection condition increases a relevance of a future subset of component forecasts. 24. An apparatus for forecasting based on time-series decomposition, the apparatus comprising: a processor decomposing an input time-series into a set of constituent frequencies; a storage device storing a set of forecaster models from which, for each constituent frequency in a subset of the set of constituent frequencies, a corresponding forecasting model is selected in a subset; the subset of forecasting models producing a set of component forecasts from which a subset of component forecasts is selected, wherein a component forecast in the subset of component forecasts is selected according to a component forecast selection condition; and a user interface to which the subset of component forecasts is outputted to revise the forecast selection condition, wherein a revised forecast selection condition increases a relevance of a future subset of component forecasts. 25. A forecasting environment for forecasting based on time-series decomposition, the environment comprising: a time-series decomposer decomposing an input time-series into a set of constituent frequencies; a storage device storing a set of forecaster models from which, for each constituent frequency in a subset of the set of constituent frequencies, a corresponding forecasting model is selected in a subset; the subset of forecasting models producing a set of component forecasts from which a subset of component forecasts is selected, wherein a component forecast in the subset of component forecasts is selected according to a component forecast selection condition; and a user interface to which the subset of component forecasts is outputted to revise the forecast selection condition, wherein a revised forecast selection condition increases a relevance of a future subset of component forecasts.