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 seriesRef: CISTER-TR-190805 Publication Date: 11 to 13, Dec, 2019
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.
58th Conference on Decision and Control (CDC 2019).
Notes: Journal to Conference paper (CISTER-TR-190502).