6 accepted papers
Open-set anomaly detection (OSAD) is an emerging paradigm designed to utilize limited labeled data from anomaly classes seen in training to identify both seen and unseen anomalies during testing. Current approaches rely on simple augmentation methods to generate pseudo anomalies that replicate unsee…
Self-supervised time series anomaly detection (TSAD) demonstrates remarkable performance improvement by extracting high-level data semantics through proxy tasks. Nonetheless, most existing self-supervised TSAD techniques rely on manual- or neural-based transformations when designing proxy tasks, ove…
Deep time series anomaly detection (TSAD) essentially relies on learning data "normality". Current approaches leverage various neural network architectures, including RNNs, CNNs, Transformers, and graph neural networks, effectively modeling temporal and inter-variable dependencies within time series…
The key to anomaly detection in time series data streams (TSDS) lies in the ability to adapt to evolving data. Active learning for anomaly detection has shown such ability by leveraging expert feedback. However, many studies in this research line strive to optimize performance by exhausting the quer…
Due to the unsupervised nature of anomaly detection, the key to fueling deep models is finding supervisory signals. Different from current reconstruction-guided generative models and transformation-based contrastive models, we devise novel data-driven supervision for tabular data by introducing a ch…
Anomalies in time series appear consecutively, forming anomaly segments. Applying the classical point-based evaluation metrics to evaluate the detection performance of segments leads to considerable underestimation, so most related studies resort to point adjustment. This operation treats all points…