Title: TSDINO: Teacher–Student Self-Distillation Framework for Robust Pre-training of Time-Series Foundation Models
Primary area: learning on time series and dynamical systems
Keywords: time series, self-distillation, time-series foundation models

Abstract: Building time-series foundation models (TSFM) poses challenges in terms of learning stability due to limited data availability and heterogeneous temporal dynamics across various time-series datasets. We propose TSDINO, a teacher-student framework for robust pre-training of TSFM based on the principle of self-distillation with no labels. TSDINO offers a model-agnostic approach that combines two complementary objectives: (i) feature preservation under augmentations and (ii) masked patch prediction. A meta-architecture comprising teacher-student networks and projection heads enables adaptation to various models. We evaluate TSDINO on classification and forecasting tasks using diverse publicly available benchmarking datasets. TSDINO consistently achieves competitive zero-shot performance over gradient-based pre-training.