Self-Supervised Contrastive Pre-Training for Multivariate Point Processes

• URL: http://arxiv.org/abs/2402.00987v1
• Date: Thu, 1 Feb 2024 20:05:04 GMT
• Title: Self-Supervised Contrastive Pre-Training for Multivariate Point Processes
• Authors: Xiao Shou, Dharmashankar Subramanian, Debarun Bhattacharjya, Tian Gao, Kristin P. Bennet
• Abstract summary: We introduce a new paradigm for self-supervised learning for multivariate point processes using a transformer encoder. Specifically, we design a novel pre-training strategy for the encoder where we not only mask random event epochs but also insert randomly sampled "void" epochs where an event does not occur. We demonstrate the effectiveness of our proposed paradigm on the next-event prediction task using synthetic datasets and 3 real applications.
• Score: 38.898053582052725
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Self-supervision is one of the hallmarks of representation learning in the increasingly popular suite of foundation models including large language models such as BERT and GPT-3, but it has not been pursued in the context of multivariate event streams, to the best of our knowledge. We introduce a new paradigm for self-supervised learning for multivariate point processes using a transformer encoder. Specifically, we design a novel pre-training strategy for the encoder where we not only mask random event epochs but also insert randomly sampled "void" epochs where an event does not occur; this differs from the typical discrete-time pretext tasks such as word-masking in BERT but expands the effectiveness of masking to better capture continuous-time dynamics. To improve downstream tasks, we introduce a contrasting module that compares real events to simulated void instances. The pre-trained model can subsequently be fine-tuned on a potentially much smaller event dataset, similar conceptually to the typical transfer of popular pre-trained language models. We demonstrate the effectiveness of our proposed paradigm on the next-event prediction task using synthetic datasets and 3 real applications, observing a relative performance boost of as high as up to 20% compared to state-of-the-art models.

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