Transfer Learning for Anomaly Detection through Localized and Unsupervised Instance Selection

Currently, time series anomaly detection is attracting significant interest. This is especially true in industry, where companies continuously monitor all aspects of production processes using various sensors. In this context, methods that automatically detect anomalous behavior in the collected data could have a large impact. Unfortunately, for a variety of reasons, it is often difficult to collect large labeled data sets for anomaly detection problems. Typically, only a few data sets will contain labeled data, and each of these will only have a very small number of labeled examples. This makes it difficult to treat anomaly detection as a supervised learning problem. In this paper, we explore using transfer learning in a time-series anomaly detection setting. Our algorithm attempts to transfer labeled examples from a source domain to a target domain where no labels are available. The approach leverages the insight that anomalies are infrequent and unexpected to decide whether or not to transfer a labeled instance to the target domain. Once the transfer is complete, we construct a nearest-neighbor classifier in the target domain, with dynamic time warping as the similarity measure. An experimental evaluation on a number of real-world data sets shows that the overall approach is promising, and that it outperforms unsupervised anomaly detection in the target domain.