Lotus: Learning to Understand Time-series by Learning to Forecast
The paper introduces forecast grounding, a pretraining principle that forces language-aligned time-series representations to predict future signal evolution before downstream tasks. LOTUS implements this by training models to generate text and simultaneously predict the next signal patch from LLM hidden states. This method preserves temporal information, allowing compact time-series language models to significantly outperform much larger counterparts.