Summary of Faith: Frequency-domain Attention in Two Horizons For Time Series Forecasting, by Ruiqi Li et al.
FAITH: Frequency-domain Attention In Two Horizons for Time Series Forecasting
by Ruiqi Li, Maowei Jiang, Kai Wang, Kaiduo Feng, Quangao Liu, Yue Sun, Xiufang Zhou
First submitted to arxiv on: 22 May 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 Frequency-domain Attention In Two Horizons (FAITH) model tackles the limitations of current deep learning-based predictive models in time series forecasting by incorporating a multi-scale sequence adaptive decomposition and fusion architecture. This approach decomposes time series into trend and seasonal components, allowing for separate processing to capture inter-channel relationships and temporal global information. FAITH’s novel feature extraction modules enable effective handling of long-term dependencies and complex patterns, outperforming existing models in various fields such as electricity, weather, and traffic. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FAITH is a new way to forecast what will happen next in things like energy usage or traffic flow. The old methods didn’t work very well because they missed important clues hidden within the data. FAITH finds these clues by breaking down the data into smaller parts and looking at each part separately. It’s like using a superpower to see patterns that others can’t. This new approach is really good at predicting what will happen in the future, even if it’s far away. |
Keywords
» Artificial intelligence » Attention » Deep learning » Feature extraction » Time series
