FAME learns to route heterogeneous time series to a budgeted subset of forecasting experts using a multidimensional forecastability fingerprint mined from validation performance, achieving 12.4% MSE reduction on a 5,000+ machine industrial dataset while activating 1.92 experts per series on average.
Timemachine: A time series is worth 4 mambas for long-term forecasting
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The paper consolidates existing research on Mamba models, their architecture variants, adaptations to different data modalities, and applications across domains.
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FAME: Forecastability-Aware Mixture of Experts for Heterogeneous Time Series Forecasting
FAME learns to route heterogeneous time series to a budgeted subset of forecasting experts using a multidimensional forecastability fingerprint mined from validation performance, achieving 12.4% MSE reduction on a 5,000+ machine industrial dataset while activating 1.92 experts per series on average.
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A Survey of Mamba
The paper consolidates existing research on Mamba models, their architecture variants, adaptations to different data modalities, and applications across domains.
- Dataset-Driven Channel Masks in Transformers for Multivariate Time Series