Summary of Learning Latent Spaces For Domain Generalization in Time Series Forecasting, by Songgaojun Deng and Maarten De Rijke
Learning Latent Spaces for Domain Generalization in Time Series Forecasting
by Songgaojun Deng, Maarten de Rijke
First submitted to arxiv on: 15 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A novel approach to time series forecasting is presented in this research paper. The authors focus on developing models that generalize well across unseen relevant domains, such as predicting web traffic data on new platforms or estimating e-commerce demand in new regions. Existing models often struggle with domain shifts due to complex temporal patterns like trends and seasonality. To address this challenge, the authors propose a method that disentangles domain-shared features using label information, allowing for a better understanding of latent temporal dependencies across domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Time series forecasting is important for many real-world applications. The challenge is to develop models that work well on new data even if it’s different from what they’ve seen before. Current models often struggle with this because time series data has complex patterns like trends and seasonality. This paper proposes a way to address this by finding common features across domains. |
Keywords
» Artificial intelligence » Time series
