Time series motifs are approximately repeating patterns in real-valued time series data. They are useful for exploratory data mining and are often used as inputs for various time series clustering, classification, segmentation, rule discovery, and visualization algorithms. Since the introduction of the first motif discovery algorithm for univariate time series in 2002, multiple efforts have been made to generalize motifs to the multidimensional case. In this work, we show that these efforts, which typically attempt to find motifs on all dimensions, will not produce meaningful motifs except in the most contrived situations. We explain this finding and introduce mSTAMP, an algorithm that allows meaningful discovery of multidimensional motifs. Beyond producing objectively and subjectively meaningful results, our algorithm has a host of additional advantages, including being much faster, requiring fewer parameters and supporting streaming data. We demonstrate the utility of our mSTAMP-based motif discovery framework on domains as diverse as audio processing, industry, and sports analytics.

In academic settings over the last decade, there has been significant progress in time series classification. However, much of this work makes assumptions that are simply unrealistic for deployed industrial applications. Examples of these unrealistic assumptions include the following: assuming that data subsequences have a single fixed-length, are precisely extracted from the data, and are correctly labeled according to their membership in a set of equal-size classes. In real-world industrial settings, these patterns can be of different lengths, the class annotations may only belong to a general region of the data, may contain errors, and finally, the class distribution is typically highly skewed. Can we learn from such weakly labeled data? In this work, we introduce SDTS, a scalable algorithm that can learn in such challenging settings. We demonstrate the utility of our ideas by learning from diverse datasets with millions of datapoints. As we shall demonstrate, our domain-agnostic parameter-free algorithm can be competitive with domain-specific algorithms used in neuroscience and entomology, even when those algorithms have been tuned by domain experts to incorporate domain knowledge.