Summary of Less Is More: Embracing Sparsity and Interpolation with Esiformer For Time Series Forecasting, by Yangyang Guo et al.
Less is more: Embracing sparsity and interpolation with Esiformer for time series forecasting
by Yangyang Guo, Yanjun Zhao, Sizhe Dang, Tian Zhou, Liang Sun, Yi Qian
First submitted to arxiv on: 8 Oct 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 paper addresses the issue of high variance and noise in time series data, which hinders accurate forecasting. To overcome this challenge, the authors propose Esiformer, a method that applies interpolation to reduce noise and alleviate periodic pattern capture. The approach incorporates a robust Sparse FFN module, enhancing model representation and robustness. Experimental results on real-world datasets demonstrate the effectiveness of Esiformer, outperforming PatchTST by 6.5% in Mean Squared Error (MSE) and 5.8% in Mean Absolute Error (MAE). The proposed method can be applied to various multivariate time series forecasting tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem with predicting what will happen in the future based on data that shows changes over time. This kind of data is noisy and hard to understand, which makes it difficult to make accurate predictions. The authors came up with a new way to clean up this noise and make predictions more accurate. They tested their method on real-world data and found that it worked better than other methods, reducing errors by 6.5% and 5.8%. This can help people in many fields like business and science make better decisions. |
Keywords
» Artificial intelligence » Mae » Mse » Time series
