  Time-series classification is an important domain of machine learning and a
plethora of methods have been developed for the task. In comparison to existing
approaches, this study presents a novel method which decomposes a time-series
dataset into latent patterns and membership weights of local segments to those
patterns. The process is formalized as a constrained objective function and a
tailored stochastic coordinate descent optimization is applied. The time-series
are projected to a new feature representation consisting of the sums of the
membership weights, which captures frequencies of local patterns. Features from
various sliding window sizes are concatenated in order to encapsulate the
interaction of patterns from different sizes. Finally, a large-scale
experimental comparison against 6 state of the art baselines and 43 real life
datasets is conducted. The proposed method outperforms all the baselines with
statistically significant margins in terms of prediction accuracy.
