Since the currently popular deep learning models are often influenced by the notorious phenomenon known as distribution shift, domain adaptation has been proposed to enhance the generalization of these models, transferring knowledge from labeled source data to unlabeled target data. Existing methods for domain adaptation primarily focus on computer vision tasks, leading to the application of models devised for image data to time series data to address the domain adaptation problem for time series data. Although these methods mitigate distribution shift to some extent, they struggle to effectively extract disentangled domain-invariant representations for time series data, resulting in suboptimal performance. To address this issue, a disentangled invariant and variant latent variable model for time series domain adaptation (DIVV) is proposed. Specifically, a causal generation process for time series data is introduced, where the latent variables are partitioned into domain-specific and domain-invariant latent variables. Based on this data generation process, the identifiability of domain-specific latent variables is established. The DIVV model, built on this identification theory, disentangles domain-specific and domain-invariant latent variables using variational influence and an orthogonal basis alignment module. Finally, the DIVV model leverages domain-invariant representations for time series classification. Experimental results demonstrate that the DIVV model outperforms existing domain adaptation methods for time series data across various benchmark datasets, highlighting its effectiveness in real-world applications.