VTG schemes for using back propagation for multivariate time series prediction

Authors:
Taeho Jo
Affiliations:
School of Information and Computer Engineering, Inha University, South Korea
Venue:
Applied Soft Computing
Year:
2013

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Abstract

This research proposes the three schemes of estimating and adding mid-terms to multivariate time series. In this research, the back propagation is adopted as the approach to multivariate time series prediction. It is traditionally designed for the task with the two models: separated model and combined model. In the proposed version of time series prediction systems, the mid-term estimator is added as the additional module to the traditional version. It is validated empirically that the three VTG (Virtual Term Generation) schemes are effective on using the back propagation for multivariate time series prediction on the four test data sets: three artificial one and a real test one.