Summary of Pathformer: Multi-scale Transformers with Adaptive Pathways For Time Series Forecasting, by Peng Chen et al.
Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting
by Peng Chen, Yingying Zhang, Yunyao Cheng, Yang Shu, Yihang Wang, Qingsong Wen, Bin Yang, Chenjuan Guo
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper proposes Pathformer, a multi-scale Transformer model that captures different characteristics spanning various scales for time series forecasting. The model integrates both temporal resolution and temporal distance to perform multi-scale modeling. It achieves this by dividing the time series into patches of varying sizes, performing dual attention over these patches to capture global correlations and local details as temporal dependencies. Additionally, the model has adaptive pathways that adjust the multi-scale modeling process based on the varying temporal dynamics of the input, improving its accuracy and generalization. The authors demonstrate the effectiveness of Pathformer through extensive experiments on eleven real-world datasets, showing it achieves state-of-the-art performance and strong generalization abilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Pathformer is a new model that helps predict future values in time series data, like stock prices or weather patterns. It’s good at capturing different patterns in the data that occur at different scales, like daily, weekly, or yearly trends. The model uses patches of varying sizes to divide up the data and then looks for connections between these patches to understand how they relate to each other. This helps it make more accurate predictions. The authors tested Pathformer on many real-world datasets and found it performed better than other models. |
Keywords
* Artificial intelligence * Attention * Generalization * Time series * Transformer
