Summary of Sparsetsf: Modeling Long-term Time Series Forecasting with 1k Parameters, by Shengsheng Lin et al.
SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters
by Shengsheng Lin, Weiwei Lin, Wentai Wu, Haojun Chen, Junjie Yang
First submitted to arxiv on: 2 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces SparseTSF, a novel lightweight model for Long-term Time Series Forecasting (LTSF) that addresses the challenges of modeling complex temporal dependencies over extended horizons with minimal computational resources. The Cross-Period Sparse Forecasting technique is at the heart of SparseTSF, simplifying the forecasting task by decoupling periodicity and trend in time series data. This technique involves downsampling original sequences to focus on cross-period trend prediction, extracting periodic features while minimizing model complexity and parameter count. The SparseTSF model uses fewer than 1k parameters to achieve competitive or superior performance compared to state-of-the-art models, showcasing remarkable generalization capabilities making it well-suited for scenarios with limited computational resources, small samples, or low-quality data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to predict future values in time series data. It’s called SparseTSF and it can work even when there isn’t much computer power available. The idea is to focus on the patterns that repeat over time and ignore the rest. This makes the model very simple, using less than 1,000 parameters. It does really well compared to other models and can handle situations where there’s not enough data or it’s of poor quality. |
Keywords
» Artificial intelligence » Generalization » Time series
