Summary of Multivariate Probabilistic Time Series Forecasting with Correlated Errors, by Vincent Zhihao Zheng et al.
Multivariate Probabilistic Time Series Forecasting with Correlated Errors
by Vincent Zhihao Zheng, Lijun Sun
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel plug-and-play method for learning the covariance structure of errors in probabilistic time series forecasting. The approach is designed to account for temporal dependencies in errors, which are often ignored in existing deep learning models. By introducing latent temporal processes and a low-rank-plus-diagonal parameterization, the method efficiently captures cross-covariance and contemporaneous covariance, respectively. Evaluation on RNN-based and Transformer-based probabilistic models demonstrates improved predictive accuracy and uncertainty quantification without increasing the model size. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better predictions for things that happen over time, like stock prices or weather forecasts. Right now, some prediction tools assume that errors (mistakes) don’t affect each other, but this isn’t always true. The authors of this paper found a way to learn how errors are connected and use that information to make more accurate predictions. |
Keywords
* Artificial intelligence * Deep learning * Rnn * Time series * Transformer
