Authors:
Yuval Shalev
and
Irad Ben-Gal
Affiliation:
Laboratory for AI, Machine Learning, Business & Data Analytics, Department of Industrial Engineering, The Tel-Aviv University, Ramat-Aviv 6997801 and Israel
Keyword(s):
Past-future Mutual Information, Context Tree, Transfer Entropy, Time Series Analysis.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Context Discovery
;
Data Analytics
;
Data Engineering
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Structured Data Analysis and Statistical Methods
;
Symbolic Systems
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.