@inproceedings{421dbd72f2b14a4a8540312c388b5c2a,
title = "Past-future mutual information estimation in sparse information conditions",
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.",
keywords = "Context Tree, Past-future Mutual Information, Time Series Analysis, Transfer Entropy",
author = "Yuval Shalev and Irad Ben-Gal",
note = "Publisher Copyright: Copyright {\textcopyright} 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved; 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2019 ; Conference date: 17-09-2019 Through 19-09-2019",
year = "2019",
doi = "10.5220/0008069300650071",
language = "אנגלית",
series = "IC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management",
publisher = "SciTePress",
pages = "65--71",
editor = "Ana Fred and Joaquim Filipe",
booktitle = "IC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management",
}