Causal Discovery in Hawkes Processes by Minimum Description Length
Keywords:Machine Learning (ML)
AbstractHawkes 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 of events in the future. Discovery of the underlying inﬂuence network among the dimensions of multi-dimensional temporal processes is of high importance in disciplines where a 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 multi-dimensional 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.
How to Cite
Jalaldoust, A., Hlaváčková-Schindler, K., & Plant, C. (2022). Causal Discovery in Hawkes Processes by Minimum Description Length. Proceedings of the AAAI Conference on Artificial Intelligence, 36(6), 6978-6987. https://doi.org/10.1609/aaai.v36i6.20656
AAAI Technical Track on Machine Learning I