Summary of Not All Frequencies Are Created Equal:towards a Dynamic Fusion Of Frequencies in Time-series Forecasting, by Xingyu Zhang et al.
Not All Frequencies Are Created Equal:Towards a Dynamic Fusion of Frequencies in Time-Series Forecasting
by Xingyu Zhang, Siyu Zhao, Zeen Song, Huijie Guo, Jianqi Zhang, Changwen Zheng, Wenwen Qiang
First submitted to arxiv on: 17 Jul 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 This paper addresses long-term time series forecasting, a crucial task in various applications, including those where methods must effectively capture long-term dependencies and be adaptable to diverse scenarios. The authors identify the limitations of current Fourier-based approaches, which typically discard high-frequency components, assuming they represent noise. However, the researchers conduct experiments revealing that certain frequencies play distinct roles depending on the scenario, sometimes improving forecasting performance when removed, while in other cases, removal is detrimental. To address this issue, they reformulate the time series forecasting problem as learning a transfer function for each frequency in the Fourier domain and design Frequency Dynamic Fusion (FreDF), which individually predicts each Fourier component before dynamically fusing the outputs. Additionally, the authors propose a novel generalization bound for time series forecasting and demonstrate that FreDF has better generalization ability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us forecast what will happen in the future based on patterns from the past. It’s like trying to predict the weather or stock prices, but instead of using just a few numbers, we look at all the patterns in the data. The problem is that some methods assume that some parts of the data are just noise and don’t matter. But this paper shows that those parts can actually be important in certain situations. To solve this problem, the researchers create a new way to predict future events by looking at each pattern separately and combining them in a special way. This method is better than previous ones because it can handle different scenarios and make more accurate predictions. |
Keywords
» Artificial intelligence » Generalization » Time series
