Time-series anomaly detection is an important research topic in data mining, popular in both academia and industry. Recently, unsupervised anomaly detection draws considerable attention, since it can detect anomalies without parameter tuning on labels and meets the demands of industrial applications. Time-series representation learning plays a vital role in addressing unsupervised anomaly detection. However, it remains challenging to learn a unified representation model with diverse distributions and handle multivariate times-series with various features.To alleviate these challenges, we propose a novel representation strategy, termed CAT-AD, for unsupervised time-series anomaly detection. It learns characteristic-aware priors for representations of time-series by incorporating embeddings of broad characteristics and is capable of handling diverse anomaly detection tasks, regardless of their lengths and dimensions.Our proposed strategy is simple yet effective, which has been verified on two univariate datasets and five multivariate datasets from public sources.

As one of the major threats to the healthy development of various online platforms, fraud has become increasingly committed in the form of gangs since collusive fraudulent activities are much easier to obtain illicit benefits with lower exposure risk. To detect fraudsters in a gang, spatio-temporal graph neural network models have been widely applied to detect both temporal and spatial collusive patterns. However, a closer peek into real-world records of fraudsters can reveal that fraud gangs usually conduct community-level camouflage, specified by two types, i.e., temporal and spatial camouflage. Such camouflage can disguise gangs as benign communities by concealing collusive patterns and thus deceiving many existing graph neural network models. In the meantime, many existing graph neural network models suffer from the challenge of extreme sample imbalance caused by rare fraudsters hidden among massive users. To handle all these challenges, in this paper, we propose a generative adversarial network framework, named Adversarial Camouflage Detector, to detect fraudsters. Concretely, this ACD framework consists of four modules, in charge of community division, camouflage identification, fraudster detection, and camouflage generation, respectively. The first three modules form up a discriminator that uses spatio-temporal graph neural networks as the foundation model and enhance fraudster detection by amplifying the gangs' collusive patterns through automatically identifying and removing camouflage. Meanwhile, the camouflage generation module plays as the generator role that generates fraudsters samples by competing against the discriminator to alleviate the challenge of sample imbalance and increase the model robustness. The experimental result shows that our proposed method outperforms other methods on real-world datasets.

A timely detection of seizures for newborn infants with electroencephalogram (EEG) has been a common yet lifesaving practice in the Neonatal Intensive Care Unit (NICU). However, it requires great human efforts for real-time monitoring, which calls for automated solutions to neonatal seizure detection. Moreover, the current automated methods focusing on adult epilepsy monitoring often fail due to (i) dynamic seizure onset location in human brains; (ii) different montages on neonates and (iii) huge distribution shift among different subjects. In this paper, we propose a deep learning framework, namely STATENet, to address the exclusive challenges with exquisite designs at the temporal, spatial and model levels. The experiments over the real-world large-scale neonatal EEG dataset illustrate that our framework achieves significantly better seizure detection performance.

This paper presents TS2Vec, a universal framework for learning representations of time series in an arbitrary semantic level. Unlike existing methods, TS2Vec performs contrastive learning in a hierarchical way over augmented context views, which enables a robust contextual representation for each timestamp. Furthermore, to obtain the representation of an arbitrary sub-sequence in the time series, we can apply a simple aggregation over the representations of corresponding timestamps. We conduct extensive experiments on time series classification tasks to evaluate the quality of time series representations. As a result, TS2Vec achieves significant improvement over existing SOTAs of unsupervised time series representation on 125 UCR datasets and 29 UEA datasets. The learned timestamp-level representations also achieve superior results in time series forecasting and anomaly detection tasks. A linear regression trained on top of the learned representations outperforms previous SOTAs of time series forecasting. Furthermore, we present a simple way to apply the learned representations for unsupervised anomaly detection, which establishes SOTA results in the literature. The source code is publicly available at https://github.com/yuezhihan/ts2vec.

Anomaly detection on multivariate time-series is of great importance in both data mining research and industrial applications. Recent approaches have achieved significant progress in this topic, but there is remaining limitations. One major limitation is that they do not capture the relationships between different time-series explicitly, resulting in inevitable false alarms. In this paper, we propose a novel self-supervised framework for multivariate time-series anomaly detection to address this issue. Our framework considers each univariate time-series as an individual feature and includes two graph attention layers in parallel to learn the complex dependencies of multivariate time-series in both temporal and feature dimensions. In addition, our approach jointly optimizes a forecasting-based model and a reconstruction-based model, obtaining better time-series representations through a combination of single-timestamp prediction and reconstruction of the entire time-series. We demonstrate the efficacy of our model through extensive experiments. The proposed method outperforms other state-of-the-art models on three real-world datasets. Further analysis shows that our method has good interpretability and is useful for anomaly diagnosis.

Multivariate time-series forecasting plays a crucial role in many real-world applications. It is a challenging problem as one needs to consider both intra-series temporal correlations and inter-series correlations simultaneously. Recently, there have been multiple works trying to capture both correlations, but most, if not all of them only capture temporal correlations in the time domain and resort to pre-defined priors as inter-series relationships. In this paper, we propose Spectral Temporal Graph Neural Network (StemGNN) to further improve the accuracy of multivariate time-series forecasting. StemGNN captures inter-series correlations and temporal dependencies jointly in the spectral domain. It combines Graph Fourier Transform (GFT) which models inter-series correlations and Discrete Fourier Transform (DFT) which models temporal dependencies in an end-to-end framework. After passing through GFT and DFT, the spectral representations hold clear patterns and can be predicted effectively by convolution and sequential learning modules. Moreover, StemGNN learns inter-series correlations automatically from the data without using pre-defined priors. We conduct extensive experiments on ten real-world datasets to demonstrate the effectiveness of StemGNN.

Large companies need to monitor various metrics (for example, Page Views and Revenue) of their applications and services in real time. At Microsoft, we develop a time-series anomaly detection service which helps customers to monitor the time-series continuously and alert for potential incidents on time. In this paper, we introduce the pipeline and algorithm of our anomaly detection service, which is designed to be accurate, efficient and general. The pipeline consists of three major modules, including data ingestion, experimentation platform and online compute. To tackle the problem of time-series anomaly detection, we propose a novel algorithm based on Spectral Residual (SR) and Convolutional Neural Network (CNN). Our work is the first attempt to borrow the SR model from visual saliency detection domain to time-series anomaly detection. Moreover, we innovatively combine SR and CNN together to improve the performance of SR model. Our approach achieves superior experimental results compared with state-of-the-art baselines on both public datasets and Microsoft production data.