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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.21884 |
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| _version_ | 1866914585497305088 |
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| author | Gervini, Daniel Kopischke, Simon A. |
| author_facet | Gervini, Daniel Kopischke, Simon A. |
| contents | This article introduces estimators of trend and seasonality for time series of point processes. We assume the point processes follow a temporal or spatial doubly-stochastic Poisson model with log-Gaussian intensity functions. The proposed estimators are computationally simple M-estimators. Their asymptotic distribution is derived, and their finite-sample performance is studied by simulation. As an example of real-data application, we study the patterns of bike demand in the Divvy bike-sharing system of the city of Chicago. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_21884 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Trend and seasonality estimation for point-process time series Gervini, Daniel Kopischke, Simon A. Methodology 62M10 This article introduces estimators of trend and seasonality for time series of point processes. We assume the point processes follow a temporal or spatial doubly-stochastic Poisson model with log-Gaussian intensity functions. The proposed estimators are computationally simple M-estimators. Their asymptotic distribution is derived, and their finite-sample performance is studied by simulation. As an example of real-data application, we study the patterns of bike demand in the Divvy bike-sharing system of the city of Chicago. |
| title | Trend and seasonality estimation for point-process time series |
| topic | Methodology 62M10 |
| url | https://arxiv.org/abs/2605.21884 |