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.

Using temporal abstraction, various forms of sampled multivariate temporal data can be transformed into a uniform representation of symbolic time intervals, from which Time Intervals Related Patterns (TIRPs) can be then discovered. Hence, mining TIRPs from symbolic time intervals offers a comprehensive framework for heterogeneous multivariate temporal data analysis. While the field of time intervals mining has gained a growing interest in recent decades, frequent closed TIRPs mining was not investigated in its full complexity. Mining frequent closed TIRPs is highly effective due to the discovery of a compact set of frequent TIRPs, which contains the complete information of all the frequent TIRPs. However, as we demonstrate in this paper, the recent advancements made in closed TIRPs discovery are incomplete, due to the discovery of only the first instances of the TIRPs within each STIs series in the database. In this paper we introduce the TIRPClo algorithm – for complete and efficient mining of frequent closed TIRPs. The algorithm utilizes a memory-efficient index and a novel method for data projection, due to which it is the first algorithm to guarantee a complete discovery of frequent closed TIRPs. In addition, a rigorous runtime comparison of TIRPClo to state-of-the-art methods is performed, demonstrating a significant speed-up on various real-world datasets.