Summary of Time-series Forecasting For Out-of-distribution Generalization Using Invariant Learning, by Haoxin Liu et al.
Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning
by Haoxin Liu, Harshavardhan Kamarthi, Lingkai Kong, Zhiyuan Zhao, Chao Zhang, B. Aditya Prakash
First submitted to arxiv on: 13 Jun 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 paper proposes an approach to improve out-of-distribution generalization abilities in time-series forecasting (TSF) models. By leveraging invariant learning, the authors aim to alleviate the inherent OOD problem in TSF. They identify two fundamental challenges: the target variables may not be sufficiently determined by the input due to unobserved core variables, and time-series datasets lack adequate environment labels. The proposed method is designed to address these challenges and improve the robustness of TSF models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps solve a big problem in predicting future events based on past data. This type of forecasting is used in many real-life situations. However, it’s tricky because the past data might not be the same as what we’ll see in the future. The researchers found two main issues: sometimes, the thing we’re trying to predict isn’t fully determined by what happened before, and we don’t have enough information about the environment or context that will happen in the future. They came up with a new way to tackle these challenges and make their predictions more reliable. |
Keywords
» Artificial intelligence » Generalization » Time series
