Summary of Learning Pattern-specific Experts For Time Series Forecasting Under Patch-level Distribution Shift, by Yanru Sun et al.
Learning Pattern-Specific Experts for Time Series Forecasting Under Patch-level Distribution Shift
by Yanru Sun, Zongxia Xie, Emadeldeen Eldele, Dongyue Chen, Qinghua Hu, Min Wu
First submitted to arxiv on: 13 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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 TFPS architecture leverages pattern-specific experts to achieve accurate and adaptable time series forecasting. The model employs a dual-domain encoder to capture both time-domain and frequency-domain features, enabling a comprehensive understanding of temporal dynamics. It then uses subspace clustering to identify distinct patterns across data patches and models these unique patterns with tailored predictions for each patch. This approach leads to improved forecasting accuracy, particularly in long-term forecasting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Time series forecasting is used to predict future values based on historical data. However, real-world time series can be complex and exhibit varying patterns. The proposed TFPS model addresses this challenge by using pattern-specific experts to make accurate predictions. It works by capturing both time-domain and frequency-domain features and then identifying distinct patterns across the data. This leads to more accurate forecasts. |
Keywords
» Artificial intelligence » Clustering » Encoder » Time series
