Summary of Wave: Weighted Autoregressive Varying Gate For Time Series Forecasting, by Jiecheng Lu et al.
WAVE: Weighted Autoregressive Varying Gate for Time Series Forecasting
by Jiecheng Lu, Xu Han, Yan Sun, Shihao Yang
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 Weighted Autoregressive Varying gatE (WAVE) attention mechanism combines Autoregressive (AR) and Moving-average (MA) components to adapt to different attention mechanisms. This allows WAVE to capture long-range and local temporal patterns in time series data, enhancing the ability of existing autoregressive attention models. The paper demonstrates that a decoder-only autoregressive Transformer model can achieve comparable results to state-of-the-art baselines for time series forecasting (TSF) tasks when using appropriate tokenization and training methods. Additionally, the authors introduce an ARMA structure into existing autoregressive attention mechanisms, incorporating indirect MA weight generation to produce implicit weights that align with modeling requirements. Experimental results show that WAVE consistently improves performance on TSF tasks, achieving state-of-the-art results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary WAVE is a new way to look at time series data. It combines two types of patterns: ones that repeat over time (autoregressive) and ones that change quickly (moving-average). This helps WAVE capture both long-term trends and short-term changes in the data. The researchers tested WAVE on a task called time series forecasting, which involves predicting future values based on past data. They found that WAVE can achieve good results when used with certain types of models and training methods. Overall, WAVE is an improvement over existing attention mechanisms for time series data. |
Keywords
» Artificial intelligence » Attention » Autoregressive » Decoder » Time series » Tokenization » Transformer
