Summary of Elastst: Towards Robust Varied-horizon Forecasting with Elastic Time-series Transformer, by Jiawen Zhang et al.
ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer
by Jiawen Zhang, Shun Zheng, Xumeng Wen, Xiaofang Zhou, Jiang Bian, Jia Li
First submitted to arxiv on: 4 Nov 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 Elastic Time-Series Transformer (ElasTST) model is designed to provide robust forecasts across various horizons, filling a gap in current time-series forecasting architectures. By incorporating non-autoregressive design with placeholders and structured self-attention masks, ElasTST ensures future outputs are invariant to adjustments in inference horizons. The model also employs rotary position embedding to capture time-series-specific periods and adaptability to different horizons, as well as a multi-scale patch design for fine-grained and coarse-grained information integration. During training, ElasTST uses a horizon reweighting strategy that approximates the effect of random sampling across multiple horizons with a single fixed horizon setting. Experimental results show the effectiveness of ElasTST’s unique design elements, positioning it as a robust solution for varied-horizon forecasting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Elastic Time-Series Transformer (ElasTST) is a new way to make predictions about things that happen at different times in the future. Right now, there are many places where we need to predict what will happen days, weeks, or months from now. But most of our current prediction tools only work well for short-term predictions and not very well for longer-term ones. The ElasTST tries to solve this problem by designing a model that can be used in different ways depending on how far ahead we want to predict. It also uses special techniques to help it understand the patterns in time-series data, which are sequences of events that happen at regular intervals. By using these techniques and a new way of training the model, ElasTST shows that it is much better than other models at making predictions for different times in the future. |
Keywords
» Artificial intelligence » Autoregressive » Embedding » Inference » Self attention » Time series » Transformer
