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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2208.10671 |
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| _version_ | 1866913665460994048 |
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| author | Idé, Tsuyoshi Kollias, Georgios Phan, Dzung T. Abe, Naoki |
| author_facet | Idé, Tsuyoshi Kollias, Georgios Phan, Dzung T. Abe, Naoki |
| contents | We propose a new sparse Granger-causal learning framework for temporal event data. We focus on a specific class of point processes called the Hawkes process. We begin by pointing out that most of the existing sparse causal learning algorithms for the Hawkes process suffer from a singularity in maximum likelihood estimation. As a result, their sparse solutions can appear only as numerical artifacts. In this paper, we propose a mathematically well-defined sparse causal learning framework based on a cardinality-regularized Hawkes process, which remedies the pathological issues of existing approaches. We leverage the proposed algorithm for the task of instance-wise causal event analysis, where sparsity plays a critical role. We validate the proposed framework with two real use-cases, one from the power grid and the other from the cloud data center management domain. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2208_10671 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Cardinality-Regularized Hawkes-Granger Model Idé, Tsuyoshi Kollias, Georgios Phan, Dzung T. Abe, Naoki Machine Learning Artificial Intelligence Methodology We propose a new sparse Granger-causal learning framework for temporal event data. We focus on a specific class of point processes called the Hawkes process. We begin by pointing out that most of the existing sparse causal learning algorithms for the Hawkes process suffer from a singularity in maximum likelihood estimation. As a result, their sparse solutions can appear only as numerical artifacts. In this paper, we propose a mathematically well-defined sparse causal learning framework based on a cardinality-regularized Hawkes process, which remedies the pathological issues of existing approaches. We leverage the proposed algorithm for the task of instance-wise causal event analysis, where sparsity plays a critical role. We validate the proposed framework with two real use-cases, one from the power grid and the other from the cloud data center management domain. |
| title | Cardinality-Regularized Hawkes-Granger Model |
| topic | Machine Learning Artificial Intelligence Methodology |
| url | https://arxiv.org/abs/2208.10671 |