Third-party cyber evaluations involving OpenAI models
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AI Research
arXiv CS.AI·May 26·5 min read
AME-TS: Anchored Mixture-of-Experts for Time Series Forecasting
TL;DR
Existing time series forecasting models struggle with diverse temporal patterns due to their shared computation paths. AME-TS, or Anchored Mixture-of-Experts, introduces a structure-guided approach that aligns expert routing with temporal characteristics.
✦ Why It Matters
Engineers can leverage AME-TS for more efficient and interpretable time series forecasting across various applications.
Key Takeaways
- Utilize AME-TS for improved time series predictions.
- Implement lightweight regime predictors for better model specialization.
- Focus on structure-aware routing to enhance model interpretability.
- Explore sparse expert models to reduce computational costs.
Full Summary
Time series forecasting often relies on large Transformer models that treat all data uniformly, which can overlook the unique temporal structures present in different datasets. AME-TS, or Anchored Mixture-of-Experts, addresses this by implementing a lightweight regime predictor that assesses series-level features such as forecastability, seasonality, trend, and sparsity.
These features inform a soft structural prior that guides the routing of tokens to specific experts during training, promoting specialization aligned with the data's temporal characteristics. In experiments on the GIFT-Eval benchmark, AME-TS demonstrated a superior balance of accuracy and efficiency, outperforming existing models at smaller scales and remaining competitive at larger scales while activating fewer parameters.
Additionally, AME-TS exhibited more interpretable routing and stable expert specialization compared to standard Mixture-of-Experts models during fine-tuning on the M5 dataset. These findings indicate that incorporating structure-aware routing can enhance the effectiveness of sparse expert models in time series forecasting.