Deep learning of point processes for modeling high-frequency data

Yoshihiro Gyotoku; Ioane Muni Toke; Nakahiro Yoshida

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arXiv · 2504.15944

Deep learning of point processes for modeling high-frequency data

Yoshihiro Gyotoku·Ioane Muni Toke·Nakahiro Yoshida

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Abstract

We investigate applications of deep neural networks to a point process having an intensity with mixing covariates processes as input. Our generic model includes Cox-type models and marked point processes as well as multivariate point processes. An oracle inequality and a rate of convergence are derived for the prediction error. A simulation study shows that the marked point process can be superior to the simple multivariate model in prediction. We apply the marked ratio model to real limit order book data

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Yoshihiro Gyotoku, Ioane Muni Toke, Nakahiro Yoshida. 2025-04-22. Deep learning of point processes for modeling high-frequency data. https://arxiv.org/abs/2504.15944

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