TSPulse: Tiny Pre-Trained Models with Disentangled Representations for Rapid Time-Series Analysis

Different time-series tasks benefit from distinct cues at various spaces and abstractions, yet existing time-series pre-trained models entangle these signals within large, monolithic embeddings, limiting transferability and zero-shot usability. Moreover, massive model sizes demand heavy compute, restricting practical deployments and real-time applications. To address this, we propose TSPulse, an ultra-light pre-trained model (1M parameters) that performs disentangled masked reconstruction across spaces and abstraction levels, explicitly learning three disentangled views: temporal embeddings for fine-grained time analysis, spectral embeddings for frequency-aware fidelity, and semantic embeddings for high-level task understanding. A hybrid masking scheme further randomizes mask style and span length to avoid pre-training bias and improve robustness. Despite its compact size, TSPulse achieves strong gains across four time-series tasks: +20\% and rank-1 on TSB-AD leaderboard benchmark for reliable anomaly detection through multi-head triangulation, which correlates complementary cues across disentangled views; +25\% in similarity search as the disentangled semantic embedding remain invariant to time, scale and noise shifts, making retrieval more robust; +50\% improvement in imputation since hybrid masking exposes the model to diverse real-world corruption patterns; and +5–16\% gains in multivariate classification with TSLens, a lightweight module that selectively attends to the most informative signals across variates. Overall, TSPulse outperform models that are 10–100× larger on 75+ datasets across tasks, while delivering state-of-the-art zero-shot results with GPU-free support and efficient fine-tuning. Models and source code will be open-sourced and currently shared in the supplementary material.