Summary of Nonstationary Time Series Forecasting Via Unknown Distribution Adaptation, by Zijian Li et al.
Nonstationary Time Series Forecasting via Unknown Distribution Adaptation
by Zijian Li, Ruichu Cai, Zhenhui Yang, Haiqin Huang, Guangyi Chen, Yifan Shen, Zhengming Chen, Xiangchen Song, Kun Zhang
First submitted to arxiv on: 20 Feb 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 The paper proposes a novel model for nonstationary time series forecasting called UDA, which detects distribution shifts and disentangles stationary and nonstationary latent variables to enable adaptation without assuming uniform distribution shifts. The model uses a Hidden Markov assumption of latent environments, which are identifiable, and leverages historical information to disentangle the variables. A variational autoencoder-based model with an autoregressive hidden Markov model estimates latent environments, while modular prior networks disentangle the variables. Experimental results on several datasets demonstrate the effectiveness of the approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to solve a big problem in time series forecasting called nonstationary distribution shifts. It’s like when you’re trying to predict what the weather will be tomorrow, but it keeps changing every hour! The solution is to make a new model that can adapt to these changes without knowing when they’ll happen. The model uses some fancy math and computer algorithms to figure out what’s going on and make better predictions. It works really well on some test datasets, which is exciting! |
Keywords
* Artificial intelligence * Autoregressive * Hidden markov model * Time series * Variational autoencoder
