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Context Based Predictive Information

Laboratory for AI, Machine Learning, Business & Data Analytics, Department of Industrial Engineering, The Tel-Aviv University, Ramat-Aviv 6997801, Israel
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Entropy 2019, 21(7), 645; https://doi.org/10.3390/e21070645
Received: 19 May 2019 / Revised: 20 June 2019 / Accepted: 25 June 2019 / Published: 29 June 2019
(This article belongs to the Section Information Theory, Probability and Statistics)
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Abstract

We propose a new algorithm called the context-based predictive information (CBPI) for estimating the predictive information (PI) between time series, by utilizing a lossy compression algorithm. The advantage of this approach over existing methods resides in the case of sparse predictive information (SPI) conditions, where the ratio between the number of informative sequences to uninformative sequences is small. It is shown that the CBPI achieves a better PI estimation than benchmark methods by ignoring uninformative sequences while improving explainability by identifying the informative sequences. We also provide an implementation of the CBPI algorithm on a real dataset of large banks’ stock prices in the U.S. In the last part of this paper, we show how the CBPI algorithm is related to the well-known information bottleneck in its deterministic version. View Full-Text
Keywords: context tree; predictive information; time series analysis; information bottleneck context tree; predictive information; time series analysis; information bottleneck
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Shalev, Y.; Ben-Gal, I. Context Based Predictive Information. Entropy 2019, 21, 645.

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