Matrix Profile IV: Using Weakly Labeled Time Series to Predict Outcomes

摘要

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.