  Finding anomalous subsequence in a long time series is a very important but
difficult problem. Existing state-of-the-art methods have been focusing on
searching for the subsequence that is the most dissimilar to the rest of the
subsequences; however, they do not take into account the background patterns
that contain the anomalous candidates. As a result, such approaches are likely
to miss local anomalies. We introduce a new definition named \textit{semantic
discord}, which incorporates the context information from larger subsequences
containing the anomaly candidates. We propose an efficient algorithm with a
derived lower bound that is up to 3 orders of magnitude faster than the brute
force algorithm in real world data. We demonstrate that our method
significantly outperforms the state-of-the-art methods in locating anomalies by
extensive experiments. We further explain the interpretability of semantic
discord.
