Summary of Flextsf: a Universal Forecasting Model For Time Series with Variable Regularities, by Jingge Xiao et al.
FlexTSF: A Universal Forecasting Model for Time Series with Variable Regularities
by Jingge Xiao, Yile Chen, Gao Cong, Wolfgang Nejdl, Simon Gottschalk
First submitted to arxiv on: 30 Oct 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 The proposed FlexTSF model is a universal time series forecasting framework that can handle diverse domains with variable regularities. It produces autoregressive forecasts and incorporates three novel designs: VT-Norm, IVP Patcher, and LED attention. The model outperforms state-of-the-art forecasting models on 12 datasets, showing excellent performance in zero-shot and few-shot settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The FlexTSF model is designed to forecast time series data from different domains. It can handle data with missing values, unequal sequence lengths, and irregular time intervals between measurements. The model uses autoregressive forecasting and includes three new features: normalization, patching, and attention. This allows the model to make predictions based on patterns it has learned from the data. |
Keywords
» Artificial intelligence » Attention » Autoregressive » Few shot » Time series » Zero shot
