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.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
TimeRFT applies reinforcement learning with multi-faceted step-wise rewards and informative sample selection to improve generalization and accuracy in TSFM adaptation beyond supervised fine-tuning.
citing papers explorer
-
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.
-
TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuning
TimeRFT applies reinforcement learning with multi-faceted step-wise rewards and informative sample selection to improve generalization and accuracy in TSFM adaptation beyond supervised fine-tuning.