Computer-implemented systems and methods for processing time series data
First Claim
1. A computer-implemented method to provide one or more model specifications using time series data that is indicative of a data generation activity occurring over a period of time, comprising:
- receiving, using one or more processors, a plurality of candidate models;
receiving a plurality of candidate input variables;
for each candidate model, determining transfer functions for the candidate input variables in order to relate a variable to be forecasted to the time series data, wherein determining transfer functions includes determining delay for each regressor;
automatically selecting for each candidate model which of the candidate input variables to include in each of the candidate models based upon the determined transfer functions; and
automatically selecting a model from the candidate models to forecast the time series data using the selected candidate input variables of the selected model.
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Abstract
Computer-implemented systems and methods for providing a forecast using time series data that is indicative of a data generation activity occurring over a period of time. Candidate models and candidate input variables are received. For each candidate model, transfer functions are determined for the candidate input variables in order to relate a variable to be forecasted to the time series data. For each candidate model there is a selection of which of the candidate input variables to include in each of the candidate models based upon the determined transfer functions. A model is selected from the candidate models to forecast the time series data using the selected input variables of the selected model.
121 Citations
28 Claims
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1. A computer-implemented method to provide one or more model specifications using time series data that is indicative of a data generation activity occurring over a period of time, comprising:
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receiving, using one or more processors, a plurality of candidate models; receiving a plurality of candidate input variables; for each candidate model, determining transfer functions for the candidate input variables in order to relate a variable to be forecasted to the time series data, wherein determining transfer functions includes determining delay for each regressor; automatically selecting for each candidate model which of the candidate input variables to include in each of the candidate models based upon the determined transfer functions; and automatically selecting a model from the candidate models to forecast the time series data using the selected candidate input variables of the selected model. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17)
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18. A computer-implemented system to provide a forecast using time series data that is indicative of a data generation activity occurring over a period of time, comprising:
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one or more processors; a computer-readable storage medium containing instructions configured to cause the one or more processors to perform operations, the instructions including; input software instructions to receive a plurality of candidate models and a plurality of candidate input variables; software instructions to determine, for each candidate model, transfer functions for the candidate input variables in order to relate a variable to be forecasted to the time series data, wherein determining transfer functions includes determining delay for each regressor; variable selection software instructions to automatically select for each candidate model which of the candidate input variables to include in each of the candidate models based upon the determined transfer functions; and model selection software instructions to automatically select a model from the candidate models to forecast the time series data using the selected candidate input variables of the selected model.
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19. Computer software stored on one or more computer-readable storage mediums, the computer software comprising program code for carrying out a method to provide a forecast using time series data that is indicative of a data generation activity occurring over a period of time, the method comprising:
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receiving a plurality of candidate models; receiving a plurality of candidate input variables; for each candidate model, determining transfer functions for the candidate input variables in order to relate a variable to be forecasted to the time series data, wherein determining transfer functions includes determining delay for each regressor; automatically selecting for each candidate model which of the candidate input variables to include in each of the candidate models based upon the determined transfer functions; and automatically selecting a model from the candidate models to forecast the time series data using the selected candidate input variables of the selected model.
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20. A computer-implemented method to provide one or more model specifications using time series data that is indicative of a data generation activity occurring over a period of time, comprising:
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receiving a plurality of candidate models; receiving a plurality of candidate input variables; for each candidate model, determining transfer functions for the candidate input variables in order to relate a variable to be forecasted to the time series data; automatically selecting for each candidate model which of the candidate input variables to include in each of the candidate models based upon the determined transfer functions, wherein the selection of the candidate input variables is based upon computing cross-correlations between residuals resulting from fitting a model for a candidate input variable and from pre-whitening the variable to be forecast using the fitted model; and automatically selecting a model from the candidate models to forecast the time series data using the selected candidate input variables of the selected model.
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21. A computer-implemented system to provide a forecast using time series data that is indicative of a data generation activity occurring over a period of time, comprising:
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one or more processors; a computer-readable storage medium containing instructions configured to cause the one or more processors to perform operations, the instructions including; input software instructions to receive a plurality of candidate models and a plurality of candidate input variables; software instructions to determine, for each candidate model, transfer functions for the candidate input variables in order to relate a variable to be forecasted to the time series data; variable selection software instructions to automatically select for each candidate model which of the candidate input variables to include in each of the candidate models based upon the determined transfer functions, wherein the selection of the candidate input variables is based upon computing cross-correlations between residuals resulting from fitting a model for a candidate input variable and from pre-whitening the variable to be forecast using the fitted model; and model selection software instructions to automatically select a model from the candidate models to forecast the time series data using the selected candidate input variables of the selected model.
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22. Computer software stored on one or more computer-readable storage mediums, the computer software comprising program code for carrying out a method to provide a forecast using time series data that is indicative of a data generation activity occurring over a period of time, the method comprising:
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receiving a plurality of candidate models; receiving a plurality of candidate input variables; for each candidate model, determining transfer functions for the candidate input variables in order to relate a variable to be forecasted to the time series data; automatically selecting for each candidate model which of the candidate input variables to include in each of the candidate models based upon the determined transfer functions, wherein the selection of the candidate input variables is based upon computing cross-correlations between residuals resulting from fitting a model for a candidate input variable and from pre-whitening the variable to be forecast using the fitted model; and automatically selecting a model from the candidate models to forecast the time series data using the selected candidate input variables of the selected model.
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23. A computer-implemented method to provide one or more model specifications using time series data that is indicative of a data generation activity occurring over a period of time, comprising:
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receiving a plurality of candidate models; receiving a plurality of candidate input variables; for each candidate model, determining transfer functions for the candidate input variables in order to relate a variable to be forecasted to the time series data; automatically selecting for each candidate model which of the candidate input variables to include in each of the candidate models based upon the determined transfer functions; performing outlier detection with respect to each of the candidate models; for a detected outlier, creating dummy regressors for use in forecasting the time series data; and automatically selecting a model from the candidate models to forecast the time series data using the selected candidate input variables of the selected model.
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24. A computer-implemented system to provide a forecast using time series data that is indicative of a data generation activity occurring over a period of time, comprising:
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one or more processors; a computer-readable storage medium containing instructions configured to cause the one or more processors to perform operations, the instructions including; input software instructions to receive a plurality of candidate models and a plurality of candidate input variables; software instructions to determine, for each candidate model, transfer functions for the candidate input variables in order to relate a variable to be forecasted to the time series data; variable selection software instructions to automatically select for each candidate model which of the candidate input variables to include in each of the candidate models based upon the determined transfer functions; outlier detection software instructions to perform outlier detection with respect to each of the candidate models, wherein for each detected outlier, dummy regressors are created for use in forecasting the time series data; and model selection software instructions to automatically select a model from the candidate models to forecast the time series data using the selected candidate input variables of the selected model.
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25. Computer software stored on one or more computer-readable storage mediums, the computer software comprising program code for carrying out a method to provide a forecast using time series data that is indicative of a data generation activity occurring over a period of time, the method comprising:
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receiving a plurality of candidate models; receiving a plurality of candidate input variables; for each candidate model, determining transfer functions for the candidate input variables in order to relate a variable to be forecasted to the time series data; automatically selecting for each candidate model which of the candidate input variables to include in each of the candidate models based upon the determined transfer functions; performing outlier detection with respect to each of the candidate models; for a detected outlier, creating dummy regressors for use in forecasting the time series data; and automatically selecting a model from the candidate models to forecast the time series data using the selected candidate input variables of the selected model.
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26. A computer-implemented method to provide one or more model specifications using time series data that is indicative of a data generation activity occurring over a period of time, comprising:
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receiving a plurality of candidate models; receiving a plurality of candidate input variables; for each candidate model, determining transfer functions for the candidate input variables in order to relate a variable to be forecasted to the time series data, wherein the plurality of candidate models includes an ARIMA reference model, and wherein determining transfer functions from an ARIMA reference model includes; determining a functional transformation and stationary transformation for each regressor, determining delay for each transformed regressor, and determining simple numerator and denominator polynomial orders for each transformed regressor; automatically selecting for each candidate model which of the candidate input variables to include in each of the candidate models based upon the determined transfer functions; and automatically selecting a model from the candidate models to forecast the time series data using the selected candidate input variables of the selected model.
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27. A computer-implemented method to provide one or more model specifications using time series data that is indicative of a data generation activity occurring over a period of time, comprising:
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receiving a plurality of candidate models; receiving a plurality of candidate input variables; for each candidate model, determining transfer functions for the candidate input variables in order to relate a variable to be forecasted to the time series data, wherein the plurality of candidate models include a white noise reference model, and wherein determining transfer functions from a white noise reference model includes; determining a functional transformation and stationary transformation for each regressor, determining delay for each transformed regressor, determining simple numerator and denominator polynomial orders for each transformed regressor, and determining the disturbance ARMA polynomials; automatically selecting for each candidate model which of the candidate input variables to include in each of the candidate models based upon the determined transfer functions; and automatically selecting a model from the candidate models to forecast the time series data using the selected candidate input variables of the selected model.
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28. A computer-implemented method to provide one or more model specifications using time series data that is indicative of a data generation activity occurring over a period of time, comprising:
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receiving a plurality of candidate models; receiving a plurality of candidate input variables; for each candidate model, determining transfer functions for the candidate input variables in order to relate a variable to be forecasted to the time series data, wherein the plurality of candidate models include a UCM reference model, and wherein determining transfer functions from a UCM reference model includes; determining a functional transformation for each regressor, determining delay for each functional transformed regressor, and determining of the level, slope, seasonal, and cycle components; automatically selecting for each candidate model which of the candidate input variables to include in each of the candidate models based upon the determined transfer functions; and automatically selecting a model from the candidate models to forecast the time series data using the selected candidate input variables of the selected model.
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Specification