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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cs.AI 2years
2026 2representative citing papers
An LLM-based agentic sampler over building knowledge graphs selects target-specific exogenous variables for zero-shot IoT forecasting, matching or beating trained baselines on three real buildings.
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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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TopoBrick: Agentic Topology Sampling of Exogenous Variables for Zero-Shot Building IoT Forecasting
An LLM-based agentic sampler over building knowledge graphs selects target-specific exogenous variables for zero-shot IoT forecasting, matching or beating trained baselines on three real buildings.