Summary of Interpretable Mixture Of Experts For Time Series Prediction Under Recurrent and Non-recurrent Conditions, by Zemian Ke et al.
Interpretable mixture of experts for time series prediction under recurrent and non-recurrent conditions
by Zemian Ke, Haocheng Duan, Sean Qian
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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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 study proposes a novel Mixture of Experts (MoE) model that improves traffic speed prediction under two distinct conditions: recurrent patterns and incident-agnostic patterns. The MoE uses separate expert models, Temporal Fusion Transformers, to capture the unique patterns of each condition. A training pipeline is developed for non-recurrent models to address limited data issues. Evaluations on a real road network show that the MoE achieves lower errors compared to benchmark algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps improve traffic speed predictions by using different models for recurring and incident-based patterns. It creates a new model called Mixture of Experts (MoE) that uses special tools to learn from these two types of patterns separately. The researchers also developed a way to train the non-recurring pattern model when there isn’t much data available. By testing this model on real traffic data, they found it was more accurate than other models. |
Keywords
» Artificial intelligence » Mixture of experts
