Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.03986 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916384636665856 |
|---|---|
| author | Xie, Yi Qiu, Tianyu Xiong, Yun Huang, Xiuqi Gao, Xiaofeng Chen, Chao |
| author_facet | Xie, Yi Qiu, Tianyu Xiong, Yun Huang, Xiuqi Gao, Xiaofeng Chen, Chao |
| contents | Time series analysis and prediction methods currently excel in quantitative analysis, offering accurate future predictions and diverse statistical indicators, but generally falling short in elucidating the underlying evolution patterns of time series. To gain a more comprehensive understanding and provide insightful explanations, we utilize symbolic regression techniques to derive explicit expressions for the non-linear dynamics in the evolution of time series variables. However, these techniques face challenges in computational efficiency and generalizability across diverse real-world time series data. To overcome these challenges, we propose \textbf{N}eural-\textbf{E}nhanced \textbf{Mo}nte-Carlo \textbf{T}ree \textbf{S}earch (NEMoTS) for time series. NEMoTS leverages the exploration-exploitation balance of Monte-Carlo Tree Search (MCTS), significantly reducing the search space in symbolic regression and improving expression quality. Furthermore, by integrating neural networks with MCTS, NEMoTS not only capitalizes on their superior fitting capabilities to concentrate on more pertinent operations post-search space reduction, but also replaces the complex and time-consuming simulation process, thereby substantially improving computational efficiency and generalizability in time series analysis. NEMoTS offers an efficient and comprehensive approach to time series analysis. Experiments with three real-world datasets demonstrate NEMoTS's significant superiority in performance, efficiency, reliability, and interpretability, making it well-suited for large-scale real-world time series data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_03986 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Efficient and Generalizable Symbolic Regression Method for Time Series Analysis Xie, Yi Qiu, Tianyu Xiong, Yun Huang, Xiuqi Gao, Xiaofeng Chen, Chao Machine Learning Time series analysis and prediction methods currently excel in quantitative analysis, offering accurate future predictions and diverse statistical indicators, but generally falling short in elucidating the underlying evolution patterns of time series. To gain a more comprehensive understanding and provide insightful explanations, we utilize symbolic regression techniques to derive explicit expressions for the non-linear dynamics in the evolution of time series variables. However, these techniques face challenges in computational efficiency and generalizability across diverse real-world time series data. To overcome these challenges, we propose \textbf{N}eural-\textbf{E}nhanced \textbf{Mo}nte-Carlo \textbf{T}ree \textbf{S}earch (NEMoTS) for time series. NEMoTS leverages the exploration-exploitation balance of Monte-Carlo Tree Search (MCTS), significantly reducing the search space in symbolic regression and improving expression quality. Furthermore, by integrating neural networks with MCTS, NEMoTS not only capitalizes on their superior fitting capabilities to concentrate on more pertinent operations post-search space reduction, but also replaces the complex and time-consuming simulation process, thereby substantially improving computational efficiency and generalizability in time series analysis. NEMoTS offers an efficient and comprehensive approach to time series analysis. Experiments with three real-world datasets demonstrate NEMoTS's significant superiority in performance, efficiency, reliability, and interpretability, making it well-suited for large-scale real-world time series data. |
| title | An Efficient and Generalizable Symbolic Regression Method for Time Series Analysis |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2409.03986 |