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Cardinality-Regularized Hawkes-Granger Model
Tsuyoshi Ide · Georgios Kollias · Dzung Phan · Naoki Abe

Tue Dec 07 08:30 AM -- 10:00 AM (PST) @ Virtual #None

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.

Author Information

Tsuyoshi Ide (IBM Research)
Georgios Kollias (International Business Machines)
Dzung Phan (IBM Research, T. J. Watson Research Center)
Naoki Abe (IBM Research AI)

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