  Subsequence-based time series classification algorithms provide accurate and
interpretable models, but training these models is extremely computation
intensive. The asymptotic time complexity of subsequence-based algorithms
remains a higher-order polynomial, because these algorithms are based on
exhaustive search for highly discriminative subsequences. Pattern sampling has
been proposed as an effective alternative to mitigate the pattern explosion
phenomenon. Therefore, we employ pattern sampling to extract discriminative
features from discretized time series data. A weighted trie is created based on
the discretized time series data to sample highly discriminative patterns.
These sampled patterns are used to identify the shapelets which are used to
transform the time series classification problem into a feature-based
classification problem. Finally, a classification model can be trained using
any off-the-shelf algorithm. Creating a pattern sampler requires a small number
of patterns to be evaluated compared to an exhaustive search as employed by
previous approaches. Compared to previously proposed algorithms, our approach
requires considerably less computational and memory resources. Experiments
demonstrate how the proposed approach fares in terms of classification accuracy
and runtime performance.
