Deep learning of point processes for modeling high-frequency data
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866916702263967744 |
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| author | Gyotoku, Yoshihiro Toke, Ioane Muni Yoshida, Nakahiro |
| author_facet | Gyotoku, Yoshihiro Toke, Ioane Muni Yoshida, Nakahiro |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15944 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep learning of point processes for modeling high-frequency data Gyotoku, Yoshihiro Toke, Ioane Muni Yoshida, Nakahiro Statistics Theory 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 |
| title | Deep learning of point processes for modeling high-frequency data |
| topic | Statistics Theory |
| url | https://arxiv.org/abs/2504.15944 |