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AdaMamba Revolutionizes Long-Term Time Series Forecasting
๐กNew Mamba variant crushes LTSF benchmarks via adaptive frequency gating. Open-source!
โก 30-Second TL;DR
What Changed
Introduces interactive patch encoding for inter-variable interaction dynamics.
Why It Matters
AdaMamba addresses cross-domain heterogeneity in time series, improving accuracy for real-world applications like finance and energy. Its efficiency preserves Mamba's scalability, making it deployable for production LTSF tasks.
What To Do Next
Clone the AdaMamba GitHub repo and benchmark it against baselines on your LTSF datasets.
Who should care:Researchers & Academics
Key Points
- โขIntroduces interactive patch encoding for inter-variable interaction dynamics.
- โขDevelops adaptive frequency-gated state-space with input-dependent frequency bases.
- โขGeneralizes temporal forgetting gate to unified time-frequency version for dynamic calibration.
- โขOutperforms SOTA on 7 public LTSF benchmarks and 2 domain-specific datasets.
- โขOpen-source implementation at https://github.com/XDjiang25/AdaMamba.
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Original source: ArXiv AI โ