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 motifs are approximately repeated patterns in real-valued temporal data. They are used for exploratory data mining methods including clustering, classification, segmentation, and rule discovery. Their current definition is limited to finding literal or near-exact matches and is unable to discover higher level semantic structure. Consider a time series generated by an accelerometer on a smartwatch. This data offers the possibility of finding motifs in human behavior. One such example is the motif generated by a handshake. Under current motif definitions, a single-pump handshake would not match a three-pump handshake, even though they are culturally and semantically equivalent events. In this work we generalize the definition of motifs to one which allows us to capture higher level semantic structure. We refer to these as time series semantic motifs. Surprisingly this increased expressiveness does not come at a great cost. Our algorithm Semantic-Motif-Finder takes approximately the same time as current state-of-the-art motif discovery algorithms. Furthermore, we demonstrate the utility of our ideas on diverse datasets.

At their core, many time series data mining algorithms can be reduced to reasoning about the shapes of time series subsequences. This requires a distance measure, and most algorithms use Euclidean Distance or Dynamic Time Warping (DTW) as their core subroutine. We argue that these distance measures are not as robust as the community believes. The undue faith in these measures derives from an overreliance on benchmark datasets and self-selection bias. The community is reluctant to address more difficult domains, for which current distance measures are ill-suited. In this work, we introduce a novel distance measure MPdist. We show that our proposed distance measure is much more robust than current distance measures. Furthermore, it allows us to successfully mine datasets that would defeat any Euclidean or DTW distance-based algorithm. Additionally, we show that our distance measure can be computed so efficiently, it allows analytics on fast streams.