Summary of Timebridge: Non-stationarity Matters For Long-term Time Series Forecasting, by Peiyuan Liu et al.
TimeBridge: Non-Stationarity Matters for Long-term Time Series Forecasting
by Peiyuan Liu, Beiliang Wu, Yifan Hu, Naiqi Li, Tao Dai, Jigang Bao, Shu-tao Xia
First submitted to arxiv on: 6 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 TimeBridge framework addresses the challenges of non-stationarity in multivariate time series forecasting by segmenting input series into smaller patches and applying Integrated Attention to mitigate short-term non-stationarity. This approach captures stable dependencies within each variate, while Cointegrated Attention preserves non-stationarity to model long-term cointegration across variates. TimeBridge achieves state-of-the-art performance in both short-term and long-term forecasting, with exceptional results in financial forecasting on the CSI 500 and S&P 500 indices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TimeBridge is a new way to predict future values in time series data that changes over time. Right now, many methods either ignore these changes or include them in their predictions without considering how they affect the result. TimeBridge takes a different approach by breaking down the data into smaller chunks and using two techniques: Integrated Attention and Cointegrated Attention. The first method helps to remove short-term fluctuations that can confuse the prediction model, while the second method preserves long-term relationships between different variables. This allows TimeBridge to make accurate predictions both in the short term and long term. |
Keywords
» Artificial intelligence » Attention » Time series
