Summary of Disentangled Interpretable Representation For Efficient Long-term Time Series Forecasting, by Yuang Zhao et al.
Disentangled Interpretable Representation for Efficient Long-term Time Series Forecasting
by Yuang Zhao, Tianyu Li, Jiadong Chen, Shenrong Ye, Fuxin Jiang, Tieying Zhang, Xiaofeng Gao
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed DiPE-Linear model addresses the challenges of Long-term Time Series Forecasting (LTSF) in Industry 5.0 by introducing a Disentangled interpretable Parameter-Efficient Linear network. This model incorporates three temporal components: Static Frequential Attention (SFA), Static Temporal Attention (STA), and Independent Frequential Mapping (IFM). The decomposed model structure reduces parameter complexity from quadratic to linear and computational complexity from quadratic to log-linear, making it a strong candidate for advancing LTSF in both research and real-world applications. DiPE-Linear demonstrates comparable or superior performance to fully connected networks (FCs) and nonlinear models across multiple open-source and real-world LTSF datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of predicting the future is being developed! This technique, called Long-term Time Series Forecasting, helps us make accurate predictions about what will happen in the long run. Right now, we’re facing some big challenges with this method because our data is really complex and we need to be very careful when making these predictions. To solve this problem, scientists created a new model called DiPE-Linear that can do three important things: it can focus on different parts of the data (like frequencies), it can pay attention to patterns in time, and it can learn how to make connections between different variables. This makes their predictions more accurate and easier to understand. The best part is that this new model works just as well or even better than other methods we’ve tried before! |
Keywords
» Artificial intelligence » Attention » Parameter efficient » Time series
