AdaMamba: Adaptive Frequency-Gated Mamba for Long-Term Time Series Forecasting

• URL: http://arxiv.org/abs/2604.23239v1
• Date: Sat, 25 Apr 2026 10:22:49 GMT
• Title: AdaMamba: Adaptive Frequency-Gated Mamba for Long-Term Time Series Forecasting
• Abstract summary: We propose AdaMamba to integrate frequency-domain analysis with temporal dependency learning.<n>AdaMamba consistently outperforms state-of-the-art methods in forecasting accu racy.
• Score: 37.92252681666789
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate long-term time series forecasting (LTSF) requires the capture of complex long-range dependencies and dynamic periodic patterns. Recent advances in frequency-domain analysis offer a global perspective for uncovering temporal characteristics. However, real-world time series often exhibit pronounced cross-domain heterogeneity where variables that appear synchronized in the time domain can differ substantially in the frequency domain. Existing frequency-based LTSF methods often rely on implicit assumptions of cross-domain homogeneity, which limits their ability to adapt to such intricate variability. To effectively integrate frequency-domain analysis with temporal dependency learning, we propose AdaMamba, a novel framework that endogenizes adaptive and context-aware frequency analysis within the Mamba state-space update process. Specifically, AdaMamba introduces an interactive patch encoding module to capture inter-variable interaction dynamics. Then, we develop an adaptive frequency-gated state-space module that generates input-dependent frequency bases, and generalizes the conventional temporal forgetting gate into a unified time-frequency forgetting gate. This allows dynamic calibration of state transitions based on learned frequency-domain importance, while preserving Mamba's capability in modeling long-range dependencies. Extensive experiments on seven public LTSF benchmarks and two domain-specific datasets demonstrate that AdaMamba consistently outperforms state-of-the-art methods in forecasting accu racy while maintaining competitive computational efficiency. The code of AdaMamba is available at https://github.com/XDjiang25/AdaMamba.

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