Summary of Learning Graph Structures and Uncertainty For Accurate and Calibrated Time-series Forecasting, by Harshavardhan Kamarthi et al.
Learning Graph Structures and Uncertainty for Accurate and Calibrated Time-series Forecasting
by Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodriguez, Chao Zhang, B Aditya Prakash
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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 The proposed paper introduces STOIC, a novel approach for multi-variate time series forecasting that tackles the challenges of unreliable relational information and uncertainty in time-series. By leveraging stochastic correlations between time-series, STOIC learns underlying structure between time-series and provides well-calibrated and accurate forecasts. The method is evaluated on a wide range of benchmark datasets, demonstrating an average improvement of 16% in accuracy and 14% in calibration compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to predict what will happen next in multiple time-series data sets. It’s called STOIC, and it works by looking at how different time series are related to each other. This helps the model make more accurate predictions. The researchers tested STOIC on many different datasets and found that it was much better than other methods at predicting what would happen next. |
Keywords
» Artificial intelligence » Time series
