Title
Transfer Information Energy: A Quantitative Causality Indicator Between Time Series
Document Type
Conference Proceeding
Department or Administrative Unit
Computer Science
Publication Date
9-11-2017
Abstract
We introduce an information-theoretical approach for analyzing cause-effect relationships between time series. Rather than using the Transfer Entropy (TE), we define and apply the Transfer Information Energy (TIE), which is based on Onicescu’s Information Energy. The TIE can substitute the TE for detecting cause-effect relationships between time series. The advantage of using the TIE is computational: we can obtain similar results, but faster. To illustrate, we compare the TIE and the TE in a machine learning application. We analyze time series of stock market indexes, with the goal to infer causal relationships between them (i.e., how they influence each other).
Recommended Citation
Caţaron, A., & Andonie, R. (2017). Transfer Information Energy: A Quantitative Causality Indicator Between Time Series. Artificial Neural Networks and Machine Learning – ICANN 2017, 512–519. https://doi.org/10.1007/978-3-319-68612-7_58
Journal
ICANN 2017: Artificial Neural Networks and Machine Learning – ICANN 2017
Rights
© Springer International Publishing AG 2017
Comments
This article was originally published in ICANN 2017: Artificial Neural Networks and Machine Learning – ICANN 2017. The full-text article from the publisher can be found here.
Due to copyright restrictions, this article is not available for free download from ScholarWorks @ CWU.