INSTINCT: Inception-based Symbolic Time Intervals series classification

Symbolic Time Intervals (STIs) describe events having a non-zero time duration, which occur in a wide range of application domains. In this paper, we target the challenge of STIs series classification (STIC), which refers to the categorization of series of STIs. Over the recent years several advancements have been made in STIC, all of which are based on either distance-metrics or feature-based traditional classifiers, mostly relying on hand-engineering of features. Due to the high computational cost of either distance calculation or feature extraction, most methods also have quite little potential to scale. We introduce INSTINCT – a novel deep learning-based framework for STIC, which 1) proposes an almost fully information-preserving transformation of raw STIs series into real matrices, and 2) presents a novel ensemble of deep inception-based convolutional neural networks for their classification. The evaluation is applied to the six real-world STIC benchmark datasets and demonstrates that INSTINCT significantly improves accuracy over seven state-of-the-art methods, as well as over three deep learning-based baselines. In addition, a comprehensive architecture study of INSTINCT is conducted as well as a scalability analysis, reporting an overall time complexity which is linear in each of the main properties of the input STIs series.