Reliable forecasting of multivariate time series under anomalous conditions is crucial in applications such as ATM cash logistics, where sudden demand shifts can disrupt operations. Modern deep forecasters achieve high accuracy on normal data but often fail when distribution shifts occur. We propose Weighted Contrastive Adaptation (WECA), a Weighted contrastive objective that aligns normal and anomaly-augmented representations, preserving anomaly-relevant information while maintaining consistency under benign variations. Evaluations on a nationwide ATM transaction dataset with domain-informed anomaly injection show that WECA improves SMAPE on anomaly-affected data by 6.1 percentage points compared to a normally trained baseline, with negligible degradation on normal data. These results demonstrate that WECA enhances forecasting reliability under anomalies without sacrificing performance during regular operations.
National University of Singapore（新加坡国立大学）；University of Hong Kong（香港大学）；JD Intelligent Cities Research（京东智能城市研究院）；JD iCity, JD Technology（京东科技京东智联云）
Deep learning models are modern tools for spatio-temporal graph (STG) forecasting. Though successful, we argue that data scarcity is a key factor limiting their recent improvements. Meanwhile, contrastive learning has been an effective method for providing self-supervision signals and addressing data scarcity in various domains. In view of this, one may ask: can we leverage the additional signals from contrastive learning to alleviate data scarcity, so as to benefit STG forecasting? To answer this question, we present the first systematic exploration on incorporating contrastive learning into …
Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series
Department of Artificial Intelligence, Kyung Hee University, Yongin, Republic of Korea（韩国成均馆大学人工智能系）；Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea（韩国大学人工智能系）
EAGLE: Contrastive Learning for Efficient Graph Anomaly Detection
Institute of Innovation, Science and Sustainability, Federation University Australia（创新、科学与可持续性研究所，联邦大学澳大利亚）；School of Software, Dalian University of Technology（软件学院，大连理工大学）；Computing Center, Anshan Normal University（计算机中心，鞍山师范学院）
STS-CCL: Spatial-Temporal Synchronous Contextual Contrastive Learning for Urban Traffic Forecasting
Zhejiang University（浙江大学）；South China University of Technology（华南理工大学）；The University of Sydney（悉尼大学）；Chongqing University（重庆大学）；Zhejiang Institute of Industry and Information Technology（浙江工业信息技术研究院）
Anomalies in univariate time series often refer to abnormal values and deviations from the temporal patterns from majority of historical observations. In multivariate time series, anomalies also refer to abnormal changes in the inter-series relationship, such as correlation, over time. Existing studies have been able to model such inter-series relationships through graph neural networks. However, most works settle on learning a static graph globally or within a context window to assist a time series forecasting task or a reconstruction task, whose objective is not tailored to explicitly detect…
DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series
Department of Data Science and AI, Monash University（数据科学与人工智能系，墨尔本大学）；Department of Computer Science, University of Oxford（计算机科学系，牛津大学）；IBM T. J. Watson Research Center（IBM T. J. Watson研究中心）；Squirrel Ai Learning（Squirrel Ai Learning公司）；School of ICT, Griffith University（信息科技学院，格里菲斯大学）
Reliable detection of anomalies is crucial when deploying machine learning models in practice, but remains challenging due to the lack of labeled data. To tackle this challenge, contrastive learning approaches are becoming increasingly popular, given the impressive results they have achieved in self-supervised representation learning settings. However, while most existing contrastive anomaly detection and segmentation approaches have been applied to images, none of them can use the contrastive losses directly for both anomaly detection and segmentation. In this paper, we close this gap by maki…
Deep Contrastive One-Class Time Series Anomaly Detection
Beihang University（北京航空航天大学）；University of New South Wales（新南威尔士大学）；Hangzhou Innovation Institute, Beihang University（北京航空航天大学杭州创新研究院）；China University of Geosciences in Beijing（中国地质大学（北京））
ContraLog: Log File Anomaly Detection with Contrastive Learning and Masked Language Modeling
Munich Center for Machine Learning（慕尼黑机器学习中心）；LMU München（慕尼黑大学）；German Research Center for Environmental Health, Helmholtz Zentrum München（德国环境健康研究中心，亥姆霍兹慕尼黑中心）
DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection
University of Chinese Academy of Science（中国科学院大学）；Tsinghua University（清华大学）；Shanghai University of International Business and Economics（上海国际商务经济学院）；Northeast Normal University（东北师范大学）；Fuzhou University（福州市大学）；Hubei University（湖北大学）；Hainan University（海南大学）；Northeastern University（东北大学）
Contrastive Representation Modeling for Anomaly Detection
Technology Innovation Institute, Abu Dhabi, UAE（阿布扎比技术创新研究所）；Computer Science Department, Khalifa University of Science and Technology, Abu Dhabi, UAE（科技学院计算机科学系）
Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review
AT&T Center for Virtualization（AT&T虚拟化中心）；Southern Methodist University（南卫理公会大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Ohio State University（俄亥俄州立大学）；Universidad Internacional Isabel I de Castilla（卡斯蒂利亚伊莎贝尔一世国际大学）；University of Florida（佛罗里达大学）；Peking University（北京大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Southern California（南加州大学）
Anomaly detection on the attributed network has recently received increasing attention in many research fields, such as cybernetic anomaly detection and financial fraud detection. With the wide application of deep learning on graph representations, existing approaches choose to apply euclidean graph encoders as their backbone, which may lose important hierarchical information, especially in complex networks. To tackle this problem, we propose an efficient anomaly detection framework using hyperbolic self-supervised contrastive learning. Specifically, we first conduct the data augmentation by p…
Monash University（莫纳什大学）；Singapore Management University（新加坡管理大学）；VinUniversity（威宁大学）；The University of Melbourne（墨尔本大学）
Cross-domain graph anomaly detection (CD-GAD) describes the problem of detecting anomalous nodes in an unlabelled target graph using auxiliary, related source graphs with labelled anomalous and normal nodes. Although it presents a promising approach to address the notoriously high false positive issue in anomaly detection, little work has been done in this line of research. There are numerous domain adaptation methods in the literature, but it is difficult to adapt them for GAD due to the unknown distributions of the anomalies and the complex node relations embedded in graph data. To this end,…
University of Amsterdam（阿姆斯特丹大学）；Ahold Delhaize（阿霍德德尔海兹）
In various business settings, there is an interest in using more complex machine learning techniques for sales forecasting. It is difficult to convince analysts, along with their superiors, to adopt these techniques since the models are considered to be "black boxes," even if they perform better than current models in use. We examine the impact of contrastive explanations about large errors on users' attitudes towards a "black-box'" model. We propose an algorithm, Monte Carlo Bounds for Reasonable Predictions. Given a large error, MC-BRP determines (1) feature values that would result in a rea…
Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective
College of Intelligence and Computing, Tianjin University（天津大学智能计算学院）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东人工智能与数字经济实验室（深圳））；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）
We present a decision support system for managing water quality in prawn ponds. The system uses various sources of data and deep learning models in a novel way to provide 24-hour forecasting and anomaly detection of water quality parameters. It provides prawn farmers with tools to proactively avoid a poor growing environment, thereby optimising growth and reducing the risk of losing stock. This is a major shift for farmers who are forced to manage ponds by reactively correcting poor water quality conditions. To our knowledge, we are the first to apply Transformer as an anomaly detection model,…
RoCA: Robust Contrastive One-class Time Series Anomaly Detection with Contaminated Data
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；Zhongguancun Laboratory（中关村实验室）；School of Software, Beihang University（北京航空航天大学软件学院）
Forecasting Anomaly Precursors via Uncertainty-Aware Time-Series Ensembles
Department of Industrial & Management Engineering, Korea University（韩国大学工业与管理工程系）；Department of Industrial Engineering, Seoul National University（首尔国立大学工业工程系）；LG CNS
RED-F: Reconstruction-Elimination based Dual-stream Contrastive Forecasting for Multivariate Time Series Anomaly Prediction
School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications（计算机学院（国家级试点软件工程学院），北京邮电大学）；China Telecom Research Institute Beijing（中国电信研究院北京分院）；Department of Computer Science, Missouri University of Science and Technology（计算机学院，密苏里科技大学）
Università degli Studi di Padova（帕多瓦大学）；Pietro Fiorentini（彼得罗·菲奥伦蒂尼公司）
An Anomaly Detection (AD) System for Self-diagnosis has been developed for Multiphase Flow Meter (MPFM). The system relies on machine learning algorithms for time series forecasting, historical data have been used to train a model and to predict the behavior of a sensor and, thus, to detect anomalies.
KTH Royal Institute of Technology（瑞典皇家理工学院）；RISE Research Institutes of Sweden（瑞典RISE研究院）
We present a representation learning framework for financial time series forecasting. One challenge of using deep learning models for finance forecasting is the shortage of available training data when using small datasets. Direct trend classification using deep neural networks trained on small datasets is susceptible to the overfitting problem. In this paper, we propose to first learn compact representations from time series data, then use the learned representations to train a simpler model for predicting time series movements. We consider a class-conditioned latent variable model. We train …
NUS（新加坡国立大学）；Institute for Infocomm Research (IR) A*STAR（新加坡科技研究局资讯通信研究院）
Anomaly detection in multi-variate time series (MVTS) data is a huge challenge as it requires simultaneous representation of long term temporal dependencies and correlations across multiple variables. More often, this is solved by breaking the complexity through modeling one dependency at a time. In this paper, we propose a Time-series Representational Learning through Contrastive Predictive Coding (TRL-CPC) towards anomaly detection in MVTS data. First, we jointly optimize an encoder, an auto-regressor and a non-linear transformation function to effectively learn the representations of the MV…
ChronoGraph: A Real-World Graph-Based Multivariate Time Series Dataset
Bitdefender；University of Bucharest（布加勒斯特大学）；University of Stuttgart（斯图加特大学）；International Max Planck Research School for Intelligent Systems（智能系统国际马克斯·普朗克研究学校）
Michigan Technological University（密歇根理工大学）；American University（美利坚大学）；Accenture Labs（埃森哲实验室）；Cognization Lab（认知化实验室）；University of Florida（佛罗里达大学）
Recently web applications have been widely used in enterprises to assist employees in providing effective and efficient business processes. Forecasting upcoming web events in enterprise web applications can be beneficial in many ways, such as efficient caching and recommendation. In this paper, we present a web event forecasting approach, DeepEvent, in enterprise web applications for better anomaly detection. DeepEvent includes three key features: web-specific neural networks to take into account the characteristics of sequential web events, self-supervised learning techniques to overcome the …
Temporal knowledge graph, serving as an effective way to store and model dynamic relations, shows promising prospects in event forecasting. However, most temporal knowledge graph reasoning methods are highly dependent on the recurrence or periodicity of events, which brings challenges to inferring future events related to entities that lack historical interaction. In fact, the current moment is often the combined effect of a small part of historical information and those unobserved underlying factors. To this end, we propose a new event forecasting model called Contrastive Event Network (CENET…
SenseTime Research（商汤科技研究院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of California, Los Angeles（加州大学洛杉矶分校）
The essence of unsupervised anomaly detection is to learn the compact distribution of normal samples and detect outliers as anomalies in testing. Meanwhile, the anomalies in real-world are usually subtle and fine-grained in a high-resolution image especially for industrial applications. Towards this end, we propose a novel framework for unsupervised anomaly detection and localization. Our method aims at learning dense and compact distribution from normal images with a coarse-to-fine alignment process. The coarse alignment stage standardizes the pixel-wise position of objects in both image and …
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
Griffith University（格里菲斯大学）；Monash University（莫纳什大学）；Squirrel AI（松鼠AI）；IDSIA（瑞士IDSIA人工智能实验室）；Università della Svizzera italiana（意大利语瑞士大学）；Politecnico di Milano（米兰理工大学）；The Chinese University of Hong Kong（香港中文大学）
King Fahd University of Petroleum & Minerals（法赫德国王石油与矿产大学）；Universiti Putra Malaysia（马来西亚博特拉大学）
Time series data is used in a wide range of real world applications. In a variety of domains , detailed analysis of time series data (via Forecasting and Anomaly Detection) leads to a better understanding of how events associated with a specific time instance behave. Time Series Analysis (TSA) is commonly performed with plots and traditional models. Machine Learning (ML) approaches , on the other hand , have seen an increase in the state of the art for Forecasting and Anomaly Detection because they provide comparable results when time and data constraints are met. A number of time series toolb…
