  Analyzing sequential data is crucial in many domains, particularly due to the
abundance of data collected from the Internet of Things paradigm. Time series
classification, the task of categorizing sequential data, has gained
prominence, with machine learning approaches demonstrating remarkable
performance on public benchmark datasets. However, progress has primarily been
in designing architectures for learning representations from raw data at fixed
(or ideal) time scales, which can fail to generalize to longer sequences. This
work introduces a \textit{compositional representation learning} approach
trained on statistically coherent components extracted from sequential data.
Based on a multi-scale change space, an unsupervised approach is proposed to
segment the sequential data into chunks with similar statistical properties. A
sequence-based encoder model is trained in a multi-task setting to learn
compositional representations from these temporal components for time series
classification. We demonstrate its effectiveness through extensive experiments
on publicly available time series classification benchmarks. Evaluating the
coherence of segmented components shows its competitive performance on the
unsupervised segmentation task.
