Modeling time series data has become a very at tractive research topic due to its wide application, such as human activity recognition, financial forecasting and sensor-based automatic system monitoring. Recently deep learning models have shown great advances in modeling the time series data but they heavily depend on a large amount of labeled data. To avoid costly labeling, this paper explores domain adaptation from a labeled source domain to the unlabeled target domain on time series data. To achieve the goal, we propose a disentangled representation learning framework named CADT to disentangle the domain-invariant features from the domain-specific ones. Particularly, CADT is injected with a novel class-wise hypersphere loss to improve the generalization of the classifier from the source domain to the target domain. Intuitively, it restricts the source data of the same class within the same hypersphere and minimizes the radius of it, which in turn enlarges the margin between different classes and makes the decision boundary of both domains easier. We further devise several kinds of domain-preserving data augmentation methods to better capture the domain-specific patterns. Extensive experiments on two public datasets and two real-world applications demonstrate the effectiveness of the proposed model against several state-of-the-art baselines.