An efficient algorithm for Harrison-Stevens forecasting using the multi-process multivariate dynamic linear model |
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| Authors: | William M. Bolstad |
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| Affiliation: | Department of Mathematics , University of Waikato , Hamilton, New Zealand |
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| Abstract: | This paper develops a computationally efficient algorithm for Harrison-Stevens forecasting in a multivariate time series which has correlated errors. The algorithm uses the observation vector one component at a time on the multiprocess multivariate dynamic linear model. This gives a computationally efficient, robust, quick adapting forecasting method for non stationary multivariate time series. |
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| Keywords: | Bayesian forecasting dynamic linear model Kalman filter mixture of distributions multi-process Kalman filter state vector state vector estimator |
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