Matrix Profile XVIII: Time Series Mining in the Face of Fast Moving Streams using a Learned Approximate Matrix Profile In recent years, the Matrix Profile has emerged as a promising approach to allow data mining on large time series archives. By efficiently computing all of the essential distance information between subsequences in a time series, the Matrix Profile makes many analytic problems, including classification and anomaly detection, easy or even trivial. However, for many tasks, in addition to archives of data, we may face neverending streams of newly arriving data. While there is an algorithm to maintain a Matrix Profile in the face of newly arriving data, it is limited to streams arriving on the order of one Hz and with small archives of historical data. However, in domains as diverse as seismology, neuroscience and entomology, we may encounter datasets that stream at rates that are orders of magnitude faster. In this work we introduce LAMP, a model that predicts, in constant time, the Matrix Profile value that would have been assigned to an incoming subsequence. This allows us to exploit the utility of the Matrix Profile in settings that would otherwise be untenable. While learning LAMP models is computationally expensive, this stage is done offline with an arbitrary computational paradigm. The models can then be deployed on resourceconstrained devices including wearable sensors. We demonstrate the utility of LAMP with experiments on diverse and challenging datasets with billions of datapoints on a simple desktop machine. We achieve more than 10000x speedup over exact methods on the same data.

Matrix Profile XVI: Efficient and Effective Labeling of Massive Time Series Archives In domains as diverse as entomology and sports medicine, analysts are routinely required to label large amounts of time series data. In a few rare cases, this can be done automatically with a classification algorithm. In many domains however, complex, noisy, and polymorphic data can defeat stateoftheart classifiers, yet easily yield to human inspection and annotation. This is especially true if the human can access auxiliary information and previous annotations. This labeling task can be a significant bottleneck in scientific progress. For example, an entomology or sports physiology lab may produce several days worth of time series each day. In this work, we introduce an algorithm that greatly reduces the human effort required. Our interactive algorithm groups subsequences and invites the user to label a groupu0027s prototype, brushing the label to all members of the group. Thus, our task reduces to optimizing the grouping(s), to allow our system to ask the fewest questions of the user. As we shall show, on diverse domains, we can reduce the human effort by at least an order of magnitude, with no decrease in accuracy.