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AI Research
arXiv CS.AI·May 25·5 min read
STM3: Mixture of Multiscale Mamba for Long-Term Spatio-Temporal Time-Series Prediction
TL;DR
Existing deep learning methods struggle to efficiently learn complex long-term spatio-temporal dependencies in time-series data. The Spatio-Temporal Mixture of Multiscale Mamba (STM3) integrates a Multiscale Mamba architecture with a Disentangled Mixture-of-Experts framework to effectively capture multiscale information and model spatial dependencies.
✦ Why It Matters
Engineers can leverage STM3 for improved accuracy in long-term time-series predictions across diverse domains.
Key Takeaways
- Utilize STM3 for enhanced long-term time-series predictions.
- Implement stable routing strategies for better representation learning.
- Explore adaptive graph causal networks for complex spatial modeling.
- Achieve state-of-the-art results in spatio-temporal prediction tasks.
Full Summary
Spatio-temporal time-series prediction has advanced rapidly, yet current deep learning techniques face challenges in capturing long-term dependencies due to the complexity of multiscale information. To address this, Spatio-Temporal Mixture of Multiscale Mamba (STM3) was developed, combining a Multiscale Mamba architecture with a Disentangled Mixture-of-Experts (DMoE) framework.
This approach allows for efficient extraction of diverse multiscale information while modeling complex spatial relationships through an adaptive graph causal network. Key innovations include a stable routing strategy and a causal contrastive learning method, which enhance representation learning and ensure scale distinguishability.
Experimental results on ten real-world benchmarks demonstrate STM3's superiority, with notable improvements on the PEMSD8 dataset, surpassing the second-best model by 7.1% in Mean Absolute Error (MAE) and 15.9% in Mean Absolute Percentage Error (MAPE). These findings suggest that STM3 can significantly enhance predictive accuracy in various applications.