Unsupervised temporal anomaly detection
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Date
2024
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Abstract
Anomaly detection becomes essential across diverse domains. Data is usually collected
sequentially in real-world applications such as sensor records and network logs. Consequently,
a major challenge in anomaly detection is the real-time volatile sequential
abnormal events. Recent research on time series has gained supreme advancements, leveraging
the vast development of deep models like recurrent neural networks and transformers.
However, most existing deep models focus on static time series while neglecting
the dynamic streaming feature inherent in real-world deployment. A critical issue arises
from the potential occurrence of distributional drift in streaming data, after which the
pre-trained models become invalid. Furthermore, as machine learning models are applied
in the safety-crucial fields like autonomous vehicles and medical diagnoses, the trustworthiness
of model predictions becomes a growing concern. A desired anomaly detector is
expected to both predict and interpret the abnormal events.
This dissertation focuses on the intersecting research area between time series and data
stream anomaly detection as well as their interpretability. We first develop a contrastivelearning-
based self-supervised approach for time series anomaly detection, contributing
to the effective representation learning of time series anomalies without labels. Subsequently,
we investigate a novel concept drift detection approach for identifying correlation
changes in the data stream. We also propose a state-transition-aware online anomaly
detection framework for data streams. Finally, we delve into the necessary properties
of time series interpreters, including cohesiveness, consistency, and robustness. We also
showcase an example-based interpreter for reconstruction-based anomaly detection
models, which provides intuitive and contrastive explanations of the reasons behind anomalies.
The proposed approaches are rigorously evaluated on various popular real-world
benchmark datasets and simulations.
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Keywords
Time series, Data stream, Anomaly detection, Interpretability