Summary of Non-parametric Probabilistic Time Series Forecasting Via Innovations Representation, by Xinyi Wang et al.
Non-parametric Probabilistic Time Series Forecasting via Innovations Representation
by Xinyi Wang, Meijen Lee, Qing Zhao, Lang Tong
First submitted to arxiv on: 5 Jun 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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 nonparametric method for probabilistic time series forecasting that leverages the classic Wiener-Kallianpur innovations representation. The approach circumvents limitations in existing parametric and semi-parametric models by introducing a machine-learning architecture and learning algorithm. A deep-learning technique and Monte Carlo sampling are used to generate a conditional probability distribution of the time series. Experiments on electricity price datasets demonstrate marked improvement over leading benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about predicting future events based on past data. It’s like trying to guess what will happen next in a movie based on what has happened so far. The problem with current methods is that they make too many assumptions and can’t handle surprises well. This new approach uses an old idea called “innovations” to create a more flexible and realistic prediction model. It’s tested on electricity price data and shows promising results. |
Keywords
* Artificial intelligence * Deep learning * Machine learning * Probability * Time series
