CURE: Context-driven Diffusion with Progressive Expansion for Single Domain Generalization in Time Series Classification

This paper studies the problem of single domain generalization in time series classification, which aims to learn a generalized time series classification model using a single source domain. This problem is highly challenging due to unreliable supervision from domain scarcity. Although current approaches employ generative models for data augmentation, these synthesized samples often suffer from low diversity and intrinsic noise, leading to weak generalization ability. Towards this end, we propose a novel approach named Context-driven Diffusion with Progressive Expansion (CURE) for single domain generalization in time series classification. The core of our CURE is to generate semantic-aware and semantic-free contexts that jointly guide a conditional diffusion model for informative data expansion. In particular, our CURE first conducts representation disentanglement to extract semantic-aware and semantic-free representations from source data. To enhance generalizability through data synthesis, we not only retrieve reference time series trajectories with similar semantics for semantic-aware contexts, but also utilize adversarial strategies to learn semantic-free contexts. These contexts are integrated as joint conditions for a diffusion model, enabling diverse and reliable virtual data. To enhance expansion adaptability and stable optimization, we progressively update our semantic-free contexts via a memory bank and measure boundary properties for dynamic data filtering. Comprehensive experiments on benchmark datasets validate the effectiveness of CURE.

Time-series AI models are often trained in one setting but used in another, such as on new people, devices, or environments. This mismatch can cause performance to drop, especially when only limited training data are available. We propose a method that expands the training data with realistic synthetic examples and keeps the most helpful ones for learning. This helps the model capture the core patterns of each activity while becoming less sensitive to domain changes. Experiments on multiple benchmark datasets show that our method performs better on unseen domains than strong baselines. This could make time-series AI systems more dependable while reducing the cost of collecting data from many sources.