Time series data remains a perennially important datatype considered in data mining. In the last decade there has been an increasing realization that time series data can best understood by reasoning about time series subsequences on the basis of their similarity to other subsequences: the two most familiar such time series concepts being motifs and discords. Time series motifs refer to two particularly close subsequences, whereas time series discords indicate subsequences that are far from their nearest neighbors. However, we argue that it can sometimes be useful to simultaneously reason about a subsequence’s closeness to certain data and its distance to other data. In this work we introduce a novel primitive called the Contrast Profile that allows us to efficiently compute such a definition in a principled way. As we will show, the Contrast Profile has many downstream uses, including anomaly detection, data exploration, and preprocessing unstructured data for classification.

Over the last decade, time series motif discovery has emerged as a useful primitive for many downstream analytical tasks, including clustering, classification, rule discovery, segmentation, and summarization. In parallel, there has been an increased understanding that Dynamic Time Warping (DTW) is the best time series similarity measure in a host of settings. Surprisingly however, there has been virtually no work on using DTW to discover motifs. The most obvious explanation of this is the fact that both motif discovery and the use of DTW can be computationally challenging, and the current best mechanisms to address their lethargy are mutually incompatible. In this work, we present the first scalable exact method to discover time series motifs under DTW. Our method automatically performs the best trade-off between time-to-compute and tightness-of-lower-bounds for a novel hierarchy of lower bounds representation we introduce. We show that under realistic settings, our algorithm can admissibly prune up to 99.99% of the DTW computations.

Chickens are the most important poultry species in the world. Globally, industrial-scale production systems account for most of the poultry meat and eggs produced. The welfare of these birds matters for both ethical and economic reasons. From an ethical perspective, poultry have a sufficient degree of awareness to suffer pain if their health is poor, or deprivation if poorly housed. From an economic viewpoint, consumers increasingly value poultry welfare, so better market access can be obtained by producers who demonstrate concern for their flocks. Recent advances in sensor technology has allowed the opportunity to record behavioral patterns in chickens, and several research groups have shown that such data can be exploited to enhance chicken welfare. However, classifying chicken behaviors poses several unique challenges which are not observed in the UCR archive or other classic benchmark collections. In particular, some behaviors are manifested in the shape of the subsequences, whereas others only in more abstract features. Most algorithms only work well for one such modality. In addition, our data of interest has classes that greatly differ in duration, and are only weakly labeled, again defying the assumptions of the classic benchmark datasets. In this work, we propose a general-purpose framework to robustly learn and classify from datasets exhibiting these issues. While our experience is with fowl, the lessons we have learned may be more generally applicable to real-world datasets in other domains including manufacturing and human health.

Time series classification is an important task in its own right, and it is often a precursor to further downstream analytics. To date, virtually all works in the literature have used either shape-based classification using a distance measure or feature-based classification after finding some suitable features for the domain. It seems to be underappreciated that in many datasets it is the case that some classes are best discriminated with features, while others are best discriminated with shape. Thus, making the shape vs. feature choice will condemn us to poor results, at least for some classes. In this work, we propose a new model for classifying time series that allows the use of both shape and feature-based measures, when warranted. Our algorithm automatically decides which approach is best for which class, and at query time chooses which classifier to trust the most. We evaluate our idea on real world datasets and demonstrate that our ideas produce statistically significant improvement in classification accuracy.