Summary of Filternet: Harnessing Frequency Filters For Time Series Forecasting, by Kun Yi et al.
FilterNet: Harnessing Frequency Filters for Time Series Forecasting
by Kun Yi, Jingru Fei, Qi Zhang, Hui He, Shufeng Hao, Defu Lian, Wei Fan
First submitted to arxiv on: 3 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
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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 paper proposes a novel approach to deep time series forecasting, addressing the limitations of Transformer-based models. The authors introduce FilterNet, a simple yet effective network that uses learnable frequency filters to extract key temporal patterns from time series signals. This is achieved through two types of filters: plain shaping and contextual shaping, which enable the model to handle high-frequency noises and utilize the entire frequency spectrum. The proposed approach outperforms state-of-the-art methods on eight time series forecasting benchmarks in terms of effectiveness and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to forecast time series data using deep learning models. It creates a special network called FilterNet that can extract important patterns from time series signals by filtering out unwanted noise. This helps the model predict future values more accurately. The authors test their approach on several datasets and show it works better than other methods. |
Keywords
» Artificial intelligence » Deep learning » Time series » Transformer
