Time series prediction using deep echo state networks

• Published in: Neural computing & applications Vol. 32; no. 23; pp. 17769 - 17787
• Main Authors: Kim, Taehwan, King, Brian R.
• Format: Journal Article
• Language: English
• Published: London Springer London 01.12.2020; Springer Nature B.V
• Subjects: Artificial Intelligence; Artificial neural networks; Chaos theory; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Forecasting; Image Processing and Computer Vision; Mathematical models; Modelling; Original Article; Performance evaluation; Probability and Statistics in Computer Science; Time series; Time series forecasting; Additive decomposition; Recurrent neural network; Echo state network; Reservoir computing
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-020-04948-x

Artificial neural networks have been used for time series modeling and forecasting in many domains. However, they are often limited in their handling of nonlinear and chaotic data. More recently, reservoir-based recurrent neural net systems, most notably echo state networks (ESN), have made substantial improvements for time series modeling. Their shallow nature lends themselves to an efficient training method, but has limitations on nonstationary, nonlinear chaotic time series, particularly large, multidimensional time series. In this paper, we propose a novel approach for forecasting time series data based on an additive decomposition (AD) applied to the time series as a preprocessor to a deep echo state network. We compare the performance of our method, AD-DeepESN, on popular neural net architectures used for time series prediction. Stationary and nonstationary data sets are used to evaluate the performance of the methods. Our results are compelling, demonstrating that AD-DeepESN has superior performance, particularly on the most challenging time series that exhibit non-stationarity and chaotic behavior compared to existing methods.