Summary of Attention As Robust Representation For Time Series Forecasting, by Peisong Niu et al.
Attention as Robust Representation for Time Series Forecasting
by PeiSong Niu, Tian Zhou, Xue Wang, Liang Sun, Rong Jin
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Transformers have revolutionized natural language processing (NLP) and computer vision (CV), and their adoption for time series forecasting is on the rise. The attention mechanism, which dynamically fuses embeddings to enhance data representation, can be leveraged as a primary representation for time series data, capitalizing on temporal relationships among data points to improve forecasting accuracy. Our study elevates attention weights as the core component of our method, using global landmarks and local windows to create a robust kernel representation that withstands noise and shifts in distribution. This approach outperforms state-of-the-art models, reducing mean squared error (MSE) in multivariate time series forecasting by 3.6% without modifying the neural network architecture. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Time series forecasting is important for many practical uses. Transformer models are good at this task because they can learn from big data and use attention to focus on important information. Our research makes attention weights more important than before, so we can get better forecasts by using them as a way to represent time series data. We tested our method with real-world data and found that it works well, even when there’s noise in the data. It’s like having a superpower for predicting what will happen next! |
Keywords
* Artificial intelligence * Attention * Mse * Natural language processing * Neural network * Nlp * Time series * Transformer
