Past-future Mutual Information Estimation in Sparse Information Conditions

Yuval Shalev, Irad Ben-Gal

2019

Abstract

We introduce the CT-PFMI, a context tree based algorithm that estimates the past-future mutual information (PFMI) between different time series. By applying a pruning phase of the context tree algorithm, uninformative past sequences are removed from PFMI estimation along with their false contributions. In situations where most of the past data is uninformative, the CT-PFMI shows better estimates to the true PFMI than other benchmark methods as demonstrated in a simulated study. By implementing CT-PFMI on real stock prices data we also demonstrate how the algorithm provides useful insights when analyzing the interactions between financial time series.

Paper Citation

in Harvard Style

Shalev Y. and Ben-Gal I. (2019). Past-future Mutual Information Estimation in Sparse Information Conditions. In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR; ISBN 978-989-758-382-7, SciTePress, pages 65-71. DOI: 10.5220/0008069300650071

in Bibtex Style

@conference{kdir19,
author={Yuval Shalev and Irad Ben-Gal},
title={Past-future Mutual Information Estimation in Sparse Information Conditions},
booktitle={Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR},
year={2019},
pages={65-71},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008069300650071},
isbn={978-989-758-382-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR
TI - Past-future Mutual Information Estimation in Sparse Information Conditions
SN - 978-989-758-382-7
AU - Shalev Y.
AU - Ben-Gal I.
PY - 2019
SP - 65
EP - 71
DO - 10.5220/0008069300650071
PB - SciTePress