Summary of Premixer: Mlp-based Pre-training Enhanced Mlp-mixers For Large-scale Traffic Forecasting, by Tongtong Zhang et al.
PreMixer: MLP-Based Pre-training Enhanced MLP-Mixers for Large-scale Traffic Forecasting
by Tongtong Zhang, Zhiyong Cui, Bingzhang Wang, Yilong Ren, Haiyang Yu, Pan Deng, Yinhai Wang
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Emerging Technologies (cs.ET)
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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 proposed PreMixer framework addresses the limitations of current models in predicting multivariate time series data from urban traffic networks. This paper tackles the challenge of forecasting traffic flow across broader geographic regions and higher temporal coverage. The framework combines a predictive model with a pre-training mechanism, both based on Multi-Layer Perceptrons (MLP). PreMixer considers temporal dependencies and spatial dynamics, and incorporates spatiotemporal positional encoding to manage heterogeneity. The innovative approach uses patch-wise MLP for masked time series modeling, learning from long-term historical data to generate enriched contextual representations. This enhances the downstream forecasting model without sacrificing computational efficiency or resource demands. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is being developed to predict traffic flow in cities. Current methods have limitations when dealing with large amounts of data and complex patterns. To solve this problem, a new framework called PreMixer was created. It uses a special type of artificial intelligence called Multi-Layer Perceptrons (MLP). This helps the model understand both the patterns that change over time and the patterns that are different in different locations. The new approach is better than others because it can learn from large amounts of data without needing to use up too many computer resources. |
Keywords
» Artificial intelligence » Positional encoding » Spatiotemporal » Time series
