Shortcomings of transfer entropy and partial transfer entropy: Extending them to escape the curse of dimensionality
| Autor: | Elsa Siggiridou, Ariadni Papana-Dagiasis, Angeliki Papana |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Multivariate statistics Applied Mathematics Confounding FOS: Physical sciences Feature selection Probability and statistics 01 natural sciences 010305 fluids & plasmas Random forest Methodology (stat.ME) Granger causality Modeling and Simulation Physics - Data Analysis Statistics and Probability 0103 physical sciences Econometrics Transfer entropy 010306 general physics Engineering (miscellaneous) Statistics - Methodology Data Analysis Statistics and Probability (physics.data-an) Curse of dimensionality Mathematics |
| DOI: | 10.48550/arxiv.2004.11760 |
| Popis: | Transfer entropy (TE) captures the directed relationships between two variables. Partial transfer entropy (PTE) accounts for the presence of all confounding variables of a multivariate system and infers only about direct causality. However, the computation of PTE involves high dimensional distributions and thus may not be robust in case of many variables. In this work, different variants of PTE are introduced, by building a reduced number of confounding variables based on different scenarios in terms of their interrelationships with the driving or response variable. Connectivity-based PTE variants and utilizing the random forests (RF) methodology are evaluated on synthetic time series. The empirical findings indicate the superiority of the suggested variants over TE and PTE, especially in case of high dimensional systems. Comment: 13 pages, 14 tables, 2 figures |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|