Researchers have explored utilizing the initial state in recurrent time series foundation models (TSFMs) as a conditioning interface, rather than leaving it unused by convention. A case study using the xLSTM-based TiRex model demonstrated that a learned spectral summary of the input window, injected into the initial state, improved accuracy. Further experiments showed that using the state as a per-dataset adaptation surface or as a channel for exogenous text information also yielded small but measurable improvements, sometimes surpassing full fine-tuning when combined with LoRA adapters. AI
IMPACT This research suggests a new avenue for improving the performance of recurrent time series models by leveraging their initial state.
RANK_REASON Research paper detailing a novel approach to conditioning recurrent time series foundation models. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- Bernhard Michael Voggenberger
- GiftEval
- Hugging Face
- LoRA+
- MASE
- multiplicative long short-term memory
- xLSTM: Extended Long Short-Term Memory
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