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A Dynamic Mode Decomposition approach with Hankel blocks to forecast multi-channel temporal series
Ref: CISTER-TR-190805       Publication Date: 11 to 13, Dec, 2019

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

Ref: CISTER-TR-190805       Publication Date: 11 to 13, Dec, 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


Events:

CDC 2019
11, Dec, 2019 >> 13, Dec, 2019
58th Conference on Decision and Control
Nice, France


58th Conference on Decision and Control (CDC 2019).
Nice, France.

Notes: Journal to Conference paper (CISTER-TR-190502).



Record Date: 29, Aug, 2019