A method for employing a deep unsupervised generative approach for disentangled factor learning is presented. The method includes decomposing, via an individual factor disentanglement component, latent variables into independent factors having different semantic meaning, enriching, via a group segment disentanglement component, group-level semantic meaning of sequential data by grouping the sequential data into a batch of segments, and generating hierarchical semantic concepts as interpretable and disentangled representations of time series data.