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Entropy-Based Discovery of Summary Causal Graphs in Time Series.
- Source :
- Entropy; Aug2022, Vol. 24 Issue 8, p1156-N.PAG, 23p
- Publication Year :
- 2022
-
Abstract
- This study addresses the problem of learning a summary causal graph on time series with potentially different sampling rates. To do so, we first propose a new causal temporal mutual information measure for time series. We then show how this measure relates to an entropy reduction principle that can be seen as a special case of the probability raising principle. We finally combine these two ingredients in PC-like and FCI-like algorithms to construct the summary causal graph. There algorithm are evaluated on several datasets, which shows both their efficacy and efficiency. [ABSTRACT FROM AUTHOR]
- Subjects :
- INFORMATION measurement
TIME series analysis
ENTROPY
PROBABILITY theory
Subjects
Details
- Language :
- English
- ISSN :
- 10994300
- Volume :
- 24
- Issue :
- 8
- Database :
- Complementary Index
- Journal :
- Entropy
- Publication Type :
- Academic Journal
- Accession number :
- 158806923
- Full Text :
- https://doi.org/10.3390/e24081156