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, 2019Abstract:
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.
Enio Filho
Paulo Lopes dos SantosEvents:
Document:
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Download58th Conference on Decision and Control (CDC 2019).
Nice, France.
Notes: Journal to Conference paper (CISTER-TR-190502).
Record Date: 29, Aug, 2019