Summary of Atfnet: Adaptive Time-frequency Ensembled Network For Long-term Time Series Forecasting, by Hengyu Ye et al.
ATFNet: Adaptive Time-Frequency Ensembled Network for Long-term Time Series Forecasting
by Hengyu Ye, Jiadong Chen, Shijin Gong, Fuxin Jiang, Tieying Zhang, Jianjun Chen, Xiaofeng Gao
First submitted to arxiv on: 8 Apr 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 The proposed ATFNet framework combines time and frequency domain representations to capture local and global dependencies in time series data. It consists of a time domain module and a frequency domain module, with the former being superior for non-periodic series and the latter ideal for periodic patterns. The framework introduces novel mechanisms such as Dominant Harmonic Series Energy Weighting, Extended DFT, and Complex-valued Spectrum Attention to enhance forecasting performance. Experimental results across multiple real-world datasets demonstrate that ATFNet outperforms current state-of-the-art methods in long-term time series forecasting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to analyze time series data is proposed, combining two different approaches: looking at the past (time domain) and looking at patterns (frequency domain). This helps find both local and global connections in the data. The team developed a special framework called ATFNet that combines these two views. They also created some new tools, like Dominant Harmonic Series Energy Weighting and Complex-valued Spectrum Attention, to make the analysis better. The results show that this new method is better than what’s currently available for predicting future values in time series data. |
Keywords
* Artificial intelligence * Attention * Time series
