STORM: A map-reduce framework for Symbolic Time Intervals series classification

Symbolic Time Intervals (STIs) represent events having a non-zero time duration, which are common in various application domains. In this article, we focus on the challenge of STIs series classification (STIC). While in the related problem of time series classification (TSC) Rocket is well-known for its exceptionally fast runtime while achieving accuracy comparable to state-of-the-art, it has only recently been studied in the field of STIC. However, since Rocket as well as its enhanced variants for TSC (e.g., MiniRocket and MultiRocket) solely rely on global features, they might not always fit best for the classification of thousands of time-units long STI series out-of-the-box, which are rather common in STIC. We introduce STORM—a novel, generic MapReduce framework for STIC, which (1) converts raw input STIs series into multivariate time series (MTS) representation; (2) partitions the converted MTS into fixed-sized blocks, each transformed independently into a uniform latent space via a common, desired Rocket variant used as a base transformation in STORM; and (3) performs sequence classification of the blocks’ transformed feature vectors via a deep, lightweight, bidirectional LSTM network. The evaluation demonstrates that STORM significantly improves accuracy over eight state-of-the-art methods for STIC either when applied with MiniRocket and MultiRocket as base transformations, as well as over the baselines of applying the respective Rocket variants directly to the converted MTS representation, that is, while also reporting overall comparable training times, on a benchmark of eight real-world STIC datasets including both extremely long and short STIs series.