Time series causality inference using echo state networks

N. Michael Mayer; Oliver Obst; Chang Yu-Chen

doi:10.1007/978-3-642-15995-4_35

Time series causality inference using echo state networks

N. Michael Mayer, Oliver Obst, Chang Yu-Chen

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

3 Citations (Scopus)

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.

Original language	English
Title of host publication	Latent Variable Analysis and Signal Separation: Proceedings 9th International Conference, LVA/ICA 2010, St. Malo, France, September 27-30, 2010
Publisher	Springer
Pages	279-286
Number of pages	8
ISBN (Print)	9783642159947
DOIs	https://doi.org/10.1007/978-3-642-15995-4_35
Publication status	Published - 2010
Event	LVA/ICA - Duration: 27 Sept 2010 → …

Publication series

Name
ISSN (Print)	0302-9743

Conference

Conference	LVA/ICA
Period	27/09/10 → …

Keywords

echo state networks
neural networks (computer science)
time-series analysis

Access to Document

10.1007/978-3-642-15995-4_35

Cite this

@inproceedings{799bfdce25854426a19e93b2fec5388b,

title = "Time series causality inference using echo state networks",

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.",

keywords = "echo state networks, neural networks (computer science), time-series analysis",

author = "Mayer, {N. Michael} and Oliver Obst and Chang Yu-Chen",

year = "2010",

doi = "10.1007/978-3-642-15995-4_35",

language = "English",

isbn = "9783642159947",

publisher = "Springer",

pages = "279--286",

booktitle = "Latent Variable Analysis and Signal Separation: Proceedings 9th International Conference, LVA/ICA 2010, St. Malo, France, September 27-30, 2010",

note = "LVA/ICA ; Conference date: 27-09-2010",

}

Time series causality inference using echo state networks. / Mayer, N. Michael; Obst, Oliver; Yu-Chen, Chang.
Latent Variable Analysis and Signal Separation: Proceedings 9th International Conference, LVA/ICA 2010, St. Malo, France, September 27-30, 2010. Springer, 2010. p. 279-286.

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

TY - GEN

T1 - Time series causality inference using echo state networks

AU - Mayer, N. Michael

AU - Obst, Oliver

AU - Yu-Chen, Chang

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

KW - echo state networks

KW - neural networks (computer science)

KW - time-series analysis

UR - http://handle.uws.edu.au:8081/1959.7/uws:35211

U2 - 10.1007/978-3-642-15995-4_35

DO - 10.1007/978-3-642-15995-4_35

M3 - Conference Paper

SN - 9783642159947

SP - 279

EP - 286

BT - Latent Variable Analysis and Signal Separation: Proceedings 9th International Conference, LVA/ICA 2010, St. Malo, France, September 27-30, 2010

PB - Springer

T2 - LVA/ICA

Y2 - 27 September 2010

ER -

Time series causality inference using echo state networks

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this