https://github.com/rose-stl-lab/autostpp

Automatic Integration for Neural Spatio-Temporal Point Process models (AI-STPP) is a new paradigm for exact, efﬁcient, non-parametric inference of point process. It is capable of learning complicated underlying intensity functions, like a damped sine wave.
https://github.com/rose-stl-lab/autostpp

probablistic-machine-learning spatiotemporal-data-analysis

Last synced: 3 months ago
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Host: GitHub
URL: https://github.com/rose-stl-lab/autostpp
Owner: Rose-STL-Lab
License: mit
Created: 2022-10-06T17:58:26.000Z (over 3 years ago)
Default Branch: main
Last Pushed: 2024-10-14T20:51:39.000Z (over 1 year ago)
Last Synced: 2025-05-09T01:33:01.703Z (about 1 year ago)
Topics: probablistic-machine-learning, spatiotemporal-data-analysis
Language: Jupyter Notebook
Homepage: https://arxiv.org/abs/2310.06179
Size: 16.7 MB
Stars: 24
Watchers: 1
Forks: 2
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

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README

          


  



Auto-STPP

✨Automatic Integration for Neural Spatiotemporal Point Process✨




    

    

    



## | Introduction

**Auto**matic Integration for Neural **S**patio-**T**emporal **P**oint **P**rocess models (Auto-STPP) is a new paradigm for exact, efﬁcient, non-parametric inference of spatiotemporal point process.

## | Citation

[[2310.06179] Automatic Integration for Spatiotemporal Neural Point Processes](https://arxiv.org/abs/2310.06179)

```

@article{zhou2023automatic,

  title={Automatic Integration for Spatiotemporal Neural Point Processes},

  author={Zhou, Zihao and Yu, Rose},

  journal={arXiv preprint arXiv:2310.06179},

  year={2023}

}

```

## | Installation

Dependencies: `make`, `conda-lock`

```bash

make create_environment

conda activate autoint-stpp

```

## | Dataset Download

```bash

python src/download_data.py

```

## | Training and Testing

Specify the parameters in `configs/autoint_stpp.yaml` and then run

```bash

make run_stpp config=autoint_stpp

```