Causal Discovery in Hawkes Processes by Minimum Description Length

Causal Discovery in Hawkes Processes by Minimum Description Length

Abstract

Hawkes processes are a special class of temporal point processes which exhibit a natural notion of causality, as occurrence of events in the past may increase the probability ofevents in the future. Discovery of the underlying influence network among the dimensions of multidimensional temporal processes is of high importance in disciplines wherea high-frequency data is to model, e.g. in financial data or in seismological data. This paper approaches the problem of learning Granger-causal network in multidimensional Hawkes processes. We formulate this problem as a model selection task in which we follow the minimum description length (MDL) principle. Moreover, we propose a general algorithm for MDL-based inference using a Monte-Carlo method and we use it for our causal discovery problem. We compare our algorithm with the state-of-the-art baseline methods on synthetic and real-world financial data. The synthetic experiments demonstrate superiority of our method in causal graph discovery compared to the baseline methods with respect to the size of the data. The results of experiments with the G-7 bonds price data are consistent with the experts’ knowledge.

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Authors
  • Jalaldoust, Amirkasra
  • Hlavackova-Schindler, Katerina
  • Plant, Claudia
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Shortfacts
Category
Paper in Conference Proceedings or in Workshop Proceedings (Paper)
Event Title
The 36th AAAI Conference on Artificial Intelligence 2022
Divisions
Data Mining and Machine Learning
Subjects
Informatik Allgemeines
Kuenstliche Intelligenz
Theoretische Informatik
Event Location
Vancouver, British Columbia, Canada
Event Type
Conference
Event Dates
February 22 - March 1, 2022
Date
February 2022
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