Title: DeCoP: Enhancing Self-Supervised Time Series Representation with Dependency Controlled Pre-training
Primary area: learning on time series and dynamical systems
Keywords: Time series analysis, self-supervised learning

Abstract: Modeling dynamic temporal dependencies is a critical challenge in time series pre-training, which evolve due to distribution shifts and multi-scale patterns. This temporal variability severely impairs the generalization of pre-trained models to downstream tasks. Existing frameworks often adopt uniform instance-level normalization, which overlooks patch-specific characteristics, and model dependencies at a single scale, failing to capture complex temporal variations. To address these limitations, we propose DeCoP, a Dependency Controlled Pre-training framework that explicitly models dynamic, multi-scale dependencies by simulating evolving inter-patch dependency. DeCoP first introduces Instance-wise Patch Normalization (IPN) to mitigate distributional shifts while preserving the unique characteristics of each patch, creating a robust foundation for representation learning. Building on this, a hierarchical Dependency Controlled Learning (DCL) strategy explicitly models inter-patch dependencies across multiple temporal scales within the latent space. This is complemented by a global Instance-level Contrastive Module (ICM), which enhances generalization by learning instance-discriminative representations from time-invariant positive pairs. DeCoP achieves state-of-the-art results on ten datasets, improving MSE by 3% on ETTh1 over PatchTST using only 37% of the FLOPs.