Semantic Discord: Finding Unusual Local Patterns for Time Series

by   Li Zhang, et al.
George Mason University

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 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.


page 1

page 2

page 3

page 4


RLAD: Time Series Anomaly Detection through Reinforcement Learning and Active Learning

We introduce a new semi-supervised, time series anomaly detection algori...

RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series

Decomposing complex time series into trend, seasonality, and remainder c...

Monte Carlo EM for Deep Time Series Anomaly Detection

Time series data are often corrupted by outliers or other kinds of anoma...

Generative Anomaly Detection for Time Series Datasets

Traffic congestion anomaly detection is of paramount importance in intel...

DeepFIB: Self-Imputation for Time Series Anomaly Detection

Time series (TS) anomaly detection (AD) plays an essential role in vario...

Anomaly Attribution of Multivariate Time Series using Counterfactual Reasoning

There are numerous methods for detecting anomalies in time series, but t...

Matching Consecutive Subpatterns Over Streaming Time Series

Pattern matching of streaming time series with lower latency under limit...

Please sign up or login with your details

Forgot password? Click here to reset