Transfer Information Energy: A Quantitative Causality Indicator Between Time Series
Department or Administrative Unit
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).
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
ICANN 2017: Artificial Neural Networks and Machine Learning – ICANN 2017
© Springer International Publishing AG 2017