back to publications overview

Time Series Causality Inference using Echo State Networks


N. Michael Mayer, Oliver Obst, and Chang Yu-Chen. Time Series Causality Inference using Echo State Networks. In Vigneron, Vincent, Zarzoso, Vicente, Moreau, Eric, Gribonval, Rémi, and Vincent, Emmanuel, editors, Ninth International Conference on Latent Variable Analysis and Signal Separation, Lecture Notes in Computer Science, pp. 279–286, Springer, Berlin, Heidelberg, 2010.


Download

[PDF] (145 kb)


Abstract

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.

BiBTeX Entry


@incollection{MOY10,
	Abstract = {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.},
	Address = {Berlin, Heidelberg},
	Author = {N. Michael Mayer and Oliver Obst and Chang Yu-Chen},
	Booktitle = {Ninth International Conference on Latent Variable
		   Analysis and Signal Separation},
	Doi = {http://dx.doi.org/10.1007/978-3-642-15995-4_35},
	Editor = {Vigneron, Vincent and Zarzoso, Vicente and Moreau, Eric
		   and Gribonval, R{\'e}mi and Vincent, Emmanuel},
	Pages = {279--286},
	Publisher = {Springer},
	Series = {Lecture Notes in Computer Science},
	Title = {Time Series Causality Inference using Echo State Networks},
	Volume = 6365,
	Year = 2010,