PESD-TSF: A Period-Aware and Explicit Structured Decomposition Framework for Long-Term Time Series Forecasting
📰 ArXiv cs.AI
Learn how PESD-TSF framework improves long-term time series forecasting by addressing periodic perception and trend-noise representation issues
Action Steps
- Apply PESD-TSF framework to your long-term time series forecasting models to improve periodic perception
- Use physics-inspired structured decomposition to address entangled trend-noise representations
- Implement channel-dependent paradigm to model cross-variable consistency in multivariate time series
- Evaluate the performance of PESD-TSF framework using metrics such as mean absolute error (MAE) and mean squared error (MSE)
- Compare the results with existing deep forecasting models to assess the improvement
Who Needs to Know This
Data scientists and researchers working on time series forecasting can benefit from this framework to improve their models' performance and accuracy
Key Insight
💡 PESD-TSF framework addresses periodic perception and trend-noise representation issues in deep forecasting models
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💡 Improve long-term time series forecasting with PESD-TSF framework! 📈
Key Takeaways
Learn how PESD-TSF framework improves long-term time series forecasting by addressing periodic perception and trend-noise representation issues
Full Article
Title: PESD-TSF: A Period-Aware and Explicit Structured Decomposition Framework for Long-Term Time Series Forecasting
Abstract:
arXiv:2605.16449v1 Announce Type: cross Abstract: Deep forecasting models often suffer from attenuated periodic perception and entangled trend-noise representations as network depth increases. Moreover, the widely adopted channel-independent paradigm, while improving training stability, disrupts intrinsic dynamic coordination among variables, hindering the modeling of cross-variable consistency in multivariate time series. To address these issues, we propose PESD-TSF, a physics-inspired structur
Abstract:
arXiv:2605.16449v1 Announce Type: cross Abstract: Deep forecasting models often suffer from attenuated periodic perception and entangled trend-noise representations as network depth increases. Moreover, the widely adopted channel-independent paradigm, while improving training stability, disrupts intrinsic dynamic coordination among variables, hindering the modeling of cross-variable consistency in multivariate time series. To address these issues, we propose PESD-TSF, a physics-inspired structur
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