A Dynamic Mode Decomposition Approach with Hankel Blocks to Forecast Multi-Channel Temporal Series

• Type: Article in International Scientific Journal
• Date: 2019

Authors

Enio Vasconcelos Filho (Author) Other; Paulo Lopes dos Santos (Author) FEUP

Journal

Title: IEEE Control Systems Letters

Vol. 3 No. 3

Pages: 739-744

Publisher: IEEE

Indexing

ISI Web of Knowledge - 13 Citations

Scopus - 6 Citations

Other information

Authenticus ID: P-00Z-AX0

DOI: 10.1109/lcsys.2019.2917811

Abstract (EN): 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 letter proposes the dynamic mode decomposition (DMD) as a tool to predict the annual air temperature and the sales of a stores' chain. The DMD 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. © 2017 IEEE.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6