Time Series Causality Inference Using Echo State Networks

One potential strength of recurrent neural networks (RNNs) is their – theoretical – ability to find a connection between cause and consequence in time series in an constraint-free manner, that is without the use of explicit probability theory. In this work we present a solution which uses the echo state approach for this purpose. Our approach learns probabilities explicitly using an online learning procedure and echo state networks. We also demonstrate the approach using a test model.

• Type: Journal
• Year: 2010
• Where: ICA
• Authors: Norbert Michael Mayer, Oliver Obst, Chang Yu-Chen
• Keywords: Echo State Approach, Explicit Probability Theory, ICA 2010, Recurrent Neural Networks, Signal Processing