Linking Micro Event History to Macro Prediction in Point Process Models
Abstract
User behaviors in social networks are microscopic with fine grained temporal information. Predicting a macroscopic quantity based on users’ collective behaviors is an important problem. However, existing works are mainly problem-specific models for the microscopic behaviors and typically design approximation or heuristic algorithms to compute the macroscopic quantity. In this paper, we propose a unifying framework with a jump stochastic differential equation model that systematically links the microscopic event data and macroscopic inference, and the theory to approximate its probability distribution. We showed that our method can better predict the user behaviors in real-world applications.
Cite
Text
Wang et al. "Linking Micro Event History to Macro Prediction in Point Process Models." International Conference on Artificial Intelligence and Statistics, 2017.Markdown
[Wang et al. "Linking Micro Event History to Macro Prediction in Point Process Models." International Conference on Artificial Intelligence and Statistics, 2017.](https://mlanthology.org/aistats/2017/wang2017aistats-linking/)BibTeX
@inproceedings{wang2017aistats-linking,
title = {{Linking Micro Event History to Macro Prediction in Point Process Models}},
author = {Wang, Yichen and Ye, Xiaojing and Zhou, Haomin and Zha, Hongyuan and Song, Le},
booktitle = {International Conference on Artificial Intelligence and Statistics},
year = {2017},
pages = {1375-1384},
url = {https://mlanthology.org/aistats/2017/wang2017aistats-linking/}
}