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A Dynamic Mode Decomposition approach with Hankel blocks to forecast multi-channel temporal series
Ref: CISTER-TR-190502       Publication Date: 2019

A Dynamic Mode Decomposition approach with Hankel blocks to forecast multi-channel temporal series

Ref: CISTER-TR-190502       Publication Date: 2019

Abstract:
Forecasting is a task with many concerns, such as the size, quality, and behavior of the data, the computing power to do it, etc. This paper proposes the Dynamic Mode Decomposition as a tool to predict the annual air temperature and the sales of a stores’ chain. The Dynamic Mode Decomposition decomposes the data into its principal modes, which are estimated from a training data set. It is assumed that the data is generated by a linear time-invariant high order autonomous system. These modes are useful to find the way the system behaves and to predict its future states, without using all the available data, even in a noisy environment. The Hankel block allows the estimation of hidden oscillatory modes, by increasing the order of the underlying dynamical system. The proposed method was tested in a case study consisting of the long term prediction of the weekly sales of a chain of stores. The performance assessment was based on the Best Fit Percentage Index. The proposed method is compared with three Neural Network Based predictors.

Authors:
Ênio Filho
,
Paulo Lopes dos Santos


Published in IEEE Control Systems Letters (L-CSS), IEEE, Volume 3, Issue 3, Article main.cssl.19-0182.56495e10, pp 739-744.

DOI:10.1109/LCSYS.2019.2917811.
ISSN: 2475-1456.



Record Date: 17, May, 2019