AdaMamba adds input-dependent frequency bases and a unified time-frequency forgetting gate to Mamba, yielding higher forecasting accuracy than prior methods on standard long-term time series benchmarks.
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Multi-source transfer learning for building thermal dynamics yields up to 63% lower forecasting errors than single-source models and outperforms time series foundation models when pretrained on 16-32 buildings over one year.
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AdaMamba: Adaptive Frequency-Gated Mamba for Long-Term Time Series Forecasting
AdaMamba adds input-dependent frequency bases and a unified time-frequency forgetting gate to Mamba, yielding higher forecasting accuracy than prior methods on standard long-term time series benchmarks.
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Thermal-GEMs: Generalized Models for Building Thermal Dynamics
Multi-source transfer learning for building thermal dynamics yields up to 63% lower forecasting errors than single-source models and outperforms time series foundation models when pretrained on 16-32 buildings over one year.