Summary of Averagelinear: Enhance Long-term Time Series Forecasting with Simple Averaging, by Gaoxiang Zhao et al.
AverageLinear: Enhance Long-Term Time series forecasting with simple averaging
by Gaoxiang Zhao, Li Zhou, Xiaoqiang Wang
First submitted to arxiv on: 30 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| 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 proposes an innovative approach to long-term time series analysis, focusing on forecasting trends by examining changes over past and future periods. The authors identify limitations in existing methods based on Transformer architecture, convolutional neural networks, or linear models when handling large numbers of channels. They introduce AverageLinear, a simple linear structure that employs channel embedding and averaging operations to capture correlations between channels while maintaining a lightweight architecture. Experimental results on real-world datasets show that AverageLinear outperforms state-of-the-art Transformer-based structures in performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to predict what will happen in the future by looking at changes over time. It finds that some methods are not good at handling lots of information. The authors create a new way to do this using simple math and it works well! They test it on real data and it’s as good or even better than other methods. |
Keywords
» Artificial intelligence » Embedding » Time series » Transformer
