Multi-step ahead forecasting using complex-valued vector autoregregression
AssigneeInternational Business Machines Corporation
InventorHiroshi Kajino
Life of this patent
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Abstract
Computer-implemented methods, computer program products, and systems are provided for multi-step ahead forecasting. A method includes configuring, by a processor device, a Vector Autoregression (VAR) model to generate a multi-step-ahead forecast based on previous observations. The previous observations are predictors and the multi-step-ahead forecast is a response to the predictors. The method further includes training, by the processor device, the VAR model using complex-valued weight parameters to avoid a training result relating to any of a divergence and a convergence to zero.
Claims25
A computer-implemented method for multi-step ahead forecasting, comprising:
- configuring, by a processor device, a Vector Autoregression (VAR) model to generate a multi-step-ahead forecast based on previous observations, where the previous observations are predictors and the multi-step-ahead forecast is a response to the predictors;
- and
- training, by the processor device, the VAR model using complex-valued weight parameters having real and imaginary values to avoid a training result relating to any of a divergence and a convergence to zero, the imaginary values used as inputs to the VAR model to generate successive predictions while only the real values are output from the VAR model without the imaginary values as the successive predictions.
A computer program product for multi-step ahead forecasting, the computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method comprising:
- configuring, by a processor device, a Vector Autoregression (VAR) model to generate a multi-step-ahead forecast based on previous observations, where the previous observations are predictors and the multi-step-ahead forecast is a response to the predictors;
- and
- training, by the processor device, the VAR model using complex-valued weight parameters having real and imaginary values to avoid a training result relating to any of a divergence and a convergence to zero, the imaginary values used as inputs to the VAR model to generate successive predictions while only the real values are output from the VAR model without the imaginary values as multi-step-ahead forecasts as the successive predictions.
A computer processing system for multi-step ahead forecasting, comprising:
- a memory device for storing program code;
- and
- a processor device for running the program code to
- configure a Vector Autoregression (VAR) model to generate a multi-step-ahead forecast based on previous observations, where the previous observations are predictors and the multi-step-ahead forecast is a response to the predictors;
- and
- train the VAR model using complex-valued weight parameters having real and imaginary values to avoid a training result relating to any of a divergence and a convergence to zero, the imaginary values used as inputs to the VAR model to generate successive predictions while only the real values are output from the VAR model without the imaginary values as multi-step-ahead forecasts as the successive predictions.
A computer-implemented method for multi-step-ahead forecasting, comprising:
- configuring, by a processor device, a Vector Autoregression (VAR) model to generate a multi-step-ahead forecast based on previous observations, where the previous observations are predictors and the multi-step ahead forecast is the response to the predictors;
- learning, by the processor device, the VAR model using a multi-step cumulative error as an objective function of the VAR model;
- and
- training the VAR model using complex-valued weight parameters having real and imaginary values to avoid a training result relating to any of a divergence and a convergence to zero, the imaginary values used as inputs to the VAR model to generate successive predictions while only the real values are output from the VAR model without the imaginary values as multi-step-ahead forecasts as the successive predictions.
A computer processing system for multi-step-ahead forecasting, comprising:
- a memory for storing program code;
- and
- a processor device for running the program code to
- configure a Vector Autoregression (VAR) model to generate a multi-step-ahead forecast based on previous observations, where the previous observations are predictors and the multi-step ahead forecast is the response to the predictors;
- learn the VAR model using a multi-step cumulative error as an objective function of the VAR model;
- and
- training the VAR model using complex-valued weight parameters having real and imaginary values to avoid a training result relating to any of a divergence and a convergence to zero, the imaginary values used as inputs to the VAR model to generate successive predictions while only the real values are output from the VAR model without the imaginary values as multi-step-ahead forecasts as the successive predictions.
Dependencies are inferred from the claim text.