DTW4TS: Learning Time Series Representations from Reliable DTW Relations

Real-world time series inherently exhibit complex temporal misalignments, posing challenges for current representation learning methods that primarily rely on rigid, point-to-point temporal matching. While Dynamic Time Warping (DTW) naturally accommodates such misalignments through nonlinear alignment, existing DTW-based methods often use DTW relations directly as supervision and mainly rely on scalar similarities, leaving relation reliability and fine-grained warping path correspondences comparatively underexplored. To address these limitations, we propose DTW4TS, a reliability-aware framework for time series representation learning. Rather than utilizing all scalar scores uniformly, DTW4TS first mitigates the impact of unreliable DTW relations by assessing local neighborhood consistency and alignment path deviation. We further introduce a lightweight DTW Adapter that encodes reliable warping paths and injects alignment information into intermediate encoder layers. In addition, an elastic contrastive objective constructs cross-sequence positive pairs from reliable temporal correspondences, encouraging the learned representations to capture elastic relationships across time series. Experiments across classification, anomaly detection, and forecasting benchmarks show that DTW4TS achieves the highest average classification accuracy and anomaly-detection F1, and the lowest average forecasting MSE, among the evaluated baselines.