Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.00946 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913375024316416 |
|---|---|
| author | Lin, Shengsheng Lin, Weiwei Wu, Wentai Chen, Haojun Yang, Junjie |
| author_facet | Lin, Shengsheng Lin, Weiwei Wu, Wentai Chen, Haojun Yang, Junjie |
| contents | This paper introduces SparseTSF, a novel, extremely lightweight model for Long-term Time Series Forecasting (LTSF), designed to address the challenges of modeling complex temporal dependencies over extended horizons with minimal computational resources. At the heart of SparseTSF lies the Cross-Period Sparse Forecasting technique, which simplifies the forecasting task by decoupling the periodicity and trend in time series data. This technique involves downsampling the original sequences to focus on cross-period trend prediction, effectively extracting periodic features while minimizing the model's complexity and parameter count. Based on this technique, the SparseTSF model uses fewer than *1k* parameters to achieve competitive or superior performance compared to state-of-the-art models. Furthermore, SparseTSF showcases remarkable generalization capabilities, making it well-suited for scenarios with limited computational resources, small samples, or low-quality data. The code is publicly available at this repository: https://github.com/lss-1138/SparseTSF. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_00946 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters Lin, Shengsheng Lin, Weiwei Wu, Wentai Chen, Haojun Yang, Junjie Machine Learning This paper introduces SparseTSF, a novel, extremely lightweight model for Long-term Time Series Forecasting (LTSF), designed to address the challenges of modeling complex temporal dependencies over extended horizons with minimal computational resources. At the heart of SparseTSF lies the Cross-Period Sparse Forecasting technique, which simplifies the forecasting task by decoupling the periodicity and trend in time series data. This technique involves downsampling the original sequences to focus on cross-period trend prediction, effectively extracting periodic features while minimizing the model's complexity and parameter count. Based on this technique, the SparseTSF model uses fewer than *1k* parameters to achieve competitive or superior performance compared to state-of-the-art models. Furthermore, SparseTSF showcases remarkable generalization capabilities, making it well-suited for scenarios with limited computational resources, small samples, or low-quality data. The code is publicly available at this repository: https://github.com/lss-1138/SparseTSF. |
| title | SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.00946 |