A JEPA-based model with domain-informed multi-view self-distillation learns light-curve representations that outperform hand-crafted features on 15 of 16 StarEmbed metrics and adapts competitively to other irregular time-series datasets.
arXiv preprint arXiv:2506.24124 , year=
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CADE framework uses direct timestep embedding and supervised contrastive alignment to improve time-series question answering, reporting gains on six tasks in the Time-MQA benchmark over LLM baselines.
STaT is a Symbolic-Temporal-Textual Alignment model that integrates three modalities to reduce shape distortion in non-stationary time series forecasting, reporting up to 8.9% gains in magnitude metrics and 8.5% less distortion on eight benchmarks.
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STaT: Resolving Shape Distortion in Non-Stationary Time Series via Tri-Modal Synergy
STaT is a Symbolic-Temporal-Textual Alignment model that integrates three modalities to reduce shape distortion in non-stationary time series forecasting, reporting up to 8.9% gains in magnitude metrics and 8.5% less distortion on eight benchmarks.