Summary of Fredf: Learning to Forecast in Frequency Domain, by Hao Wang et al.
FreDF: Learning to Forecast in Frequency Domain
by Hao Wang, Licheng Pan, Zhichao Chen, Degui Yang, Sen Zhang, Yifei Yang, Xinggao Liu, Haoxuan Li, Dacheng Tao
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Applications (stat.AP); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
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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 a critical limitation in current time series modeling approaches, which often overlook the autocorrelation present in both historical and label sequences. Specifically, direct forecast (DF) models assume conditional independence within the label sequence, neglecting its inherent autocorrelation. The authors propose Frequency-enhanced Direct Forecast (FreDF), a novel approach that bypasses the complexity of label autocorrelation by learning to forecast in the frequency domain. FreDF is shown to outperform existing state-of-the-art methods, including iTransformer, and is compatible with various forecast models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers try to improve time series modeling. They notice that current approaches don’t consider how events are connected over time, which can lead to poor predictions. The authors create a new way of forecasting called FreDF, which looks at the frequency of events instead of just looking at when they happen. This approach works better than other methods and can be used with different types of forecasts. |
Keywords
* Artificial intelligence * Time series
