WEIGHT ADJUSTED COMPOSITE MODEL FOR FORECASTING IN ANOMALOUS ENVIRONMENTS
First 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.
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Accused Products
Abstract
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.
11 Citations
20 Claims
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1. A method for event forecasting in anomalous environments, the method comprising:
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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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A computer program product for event forecasting in anomalous environments, the computer program product comprising:
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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. - View Dependent Claims (14, 15, 16, 17, 18, 19)
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20. A computer system for event forecasting in anomalous environments, the computer system comprising:
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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.
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Specification