With the growing adoption of time-series anomaly detection (TAD) technology, numerous studies have employed deep learning-based detectors to analyze time-series data in the fields of Internet services, industrial systems, and sensors. The selection and optimization of anomaly detectors strongly rely on the availability of an effective evaluation for TAD performance. Since anomalies in time-series data often manifest as a sequence of points, conventional metrics that solely consider the detection of individual points are inadequate. Existing TAD evaluators typically employ point-based or event-based metrics to capture the temporal context. However, point-based evaluators tend to overestimate detectors that excel only in detecting long anomalies, while event-based evaluators are susceptible to being misled by fragmented detection results. To address these limitations, we propose OIPR1, a novel TAD evaluator with area-based metrics. It models the process of operators receiving detector alarms and handling anomalies, utilizing area under the operator interest curve to evaluate TAD performance. Furthermore, we build a special scenario dataset to compare the characteristics of different evaluators. Through experiments conducted on the special scenario dataset and five real-world datasets, we demon-strate the remarkable performance of OIPR in extreme and complex scenarios. It achieves a balance between point and event perspectives, overcoming their primary limitations and offering applicability to broader situations.

Human experts typically integrate numerical and textual multimodal information to analyze time series. However, most traditional deep learning predictors rely solely on unimodal numerical data, using a fixed-length window for training and prediction on a single dataset, and cannot adapt to different scenarios. The powered pre-trained large language model has introduced new opportunities for time series analysis. Yet, existing methods are either inefficient in training, incapable of handling textual information, or lack zero-shot forecasting capability. In this paper, we innovatively model time series as a foreign language and construct ChatTime, a unified framework for time series and text processing. As an out-of-the-box multimodal time series foundation model, ChatTime provides zero-shot forecasting capability and supports bimodal input/output for both time series and text. We design a series of experiments to verify the superior performance of ChatTime across multiple tasks and scenarios, and create four multimodal datasets to address data gaps. The experimental results demonstrate the potential and utility of ChatTime.

Accurate forecasting of time series is crucial for many applications in the real world. Conventional methods primarily rely on statistical analysis of historical data, often leading to overfitting and failing to account for background information and constraints imposed by external events. Therefore, introducing large language models (LLMs) with robust textual capabilities holds significant potential. However, due to the inherent limitations of LLMs in handling numerical data, they do not exhibit advantages in precise numerical prediction tasks. Therefore, we propose a framework to integrate LLMs with conventional methods synergistically. Rather than directly outputting numerical predictions, we leverage the capabilities of the LLMs to generate textual temporal patterns, thereby fully utilizing their inherent knowledge and reasoning abilities. Additionally, we introduce a memory network designed to decode these textual representations into a format that numerical models can effectively interpret. This approach not only capitalizes on the strengths of the LLM in text processing but also bridges the gap between textual and numerical data, enhancing the overall predictive performance of the model. Our experimental results demonstrate the framework's effectiveness, achieving state-of-the-art performance on various benchmark datasets.

Recently, reconstruction-based deep models have gained widespread usage in unsupervised anomaly detection. However, they may overlook some anomalies owing to the over-generalization of neural networks. Several studies have incorporated memory networks to mitigate this problem. Nonetheless, some of them lack an explicit memory updating process, while others rely on data-driven updating methods that are sensitive to initial values and unsuitable for end-to-end training. Additionally, the traditional criterion for detection computed in the high-dimensional input space may collapse as the spike in the deviation score is averaged across numerous dimensions. To address these challenges, we propose MCAKE, a M emory-augmented C ontrastive A utoencoder with K NN-Based E xtraction. It is designed to highlight the deviation score for anomalies by reconstructing input using fixed normal prototypes recorded in the memory. We explicitly encourage the memory to be autonomously learned and effectively allocated through contrastive learning with multiple positive and multiple negative samples. Furthermore, we introduce a bivariate detection criterion that calculates anomaly scores considering both input and latent space to tackle the collapse. Extensive experiments on 50 datasets across various categories demonstrate the superiority of our approach, with a 2% relative improvement over the previous state-of-the-art models.

Anomaly detection in multivariate time series (MTS) is crucial for various applications in data mining and industry. Current industrial methods typically approach anomaly detection as an unsupervised learning task, aiming to identify deviations by estimating the normal distribution in noisy, label-free datasets. These methods increasingly incorporate interdependencies between channels through graph structures to enhance accuracy. However, the role of interdependencies is more critical than previously understood, as shifts in interdependencies between MTS channels from normal to anomalous data are significant. This observation suggests that anomalies could be detected by changes in these interdependency graph series. To capitalize on this insight, we introduce MADGA (MTS Anomaly Detection via Graph Alignment), which redefines anomaly detection as a graph alignment (GA) problem that explicitly utilizes interdependencies for anomaly detection. MADGA dynamically transforms subsequences into graphs to capture the evolving interdependencies, and Graph alignment is performed between these graphs, optimizing an alignment plan that minimizes cost, effectively minimizing the distance for normal data and maximizing it for anomalous data. Uniquely, our GA approach involves explicit alignment of both nodes and edges, employing Wasserstein distance for nodes and Gromov-Wasserstein distance for edges. To our knowledge, this is the first application of GA to MTS anomaly detection that explicitly leverages interdependency for this purpose. Extensive experiments on diverse real-world datasets validate the effectiveness of MADGA, demonstrating its capability to detect anomalies and differentiate interdependencies, consistently achieving state-of-the-art across various scenarios.

Time series forecasting has played a pivotal role across various industries, including finance, transportation, energy, healthcare, and climate. Due to the abundant seasonal information they contain, timestamps possess the potential to offer robust global guidance for forecasting techniques. However, existing works primarily focus on local observations, with timestamps being treated merely as an optional supplement that remains underutilized. When data gathered from the real world is polluted, the absence of global information will damage the robust prediction capability of these algorithms. To address these problems, we propose a novel framework named GLAFF. Within this framework, the timestamps are modeled individually to capture the global dependencies. Working as a plugin, GLAFF adaptively adjusts the combined weights for global and local information, enabling seamless collaboration with any time series forecasting backbone. Extensive experiments conducted on nine real-world datasets demonstrate that GLAFF significantly enhances the average performance of widely used mainstream forecasting models by 12.5\%, surpassing the previous state-of-the-art method by 5.5\%.

Artificial Intelligence for IT Operations (AIOps) for microservice systems has attracted much attention in academia and industry, aiming to reduce the burden of operations developers and improve the reliability of microservices. However, due to mostly private datasets and unique data requirements of different studies, researchers are forced to invest considerable effort in tedious tasks such as data simulation and data collection, which prevents them from concentrating on model development. To tackle this dilemma, we introduce MicroOps, a microservice data simulation and AIOps model development platform. MicroOps provides full-process automation support for microservice AIOps research, with key roles for rapid dataset generation and intuitive model testing. Based on MicroOps, we release two multimodal datasets collected from two widely used microservice systems. A user survey is conducted on MicroOps, evaluating its usability and practicality through the System Usability Scale (SUS) and open-ended questions. The results show that both are highly positively rated. Platform: https://github.com/OpenNetAI/MicroOps.

Many unsupervised methods have recently been proposed for multivariate time series anomaly detection. However, existing works mainly focus on stable data yet often omit the drift generated from non-stationary environments, which may lead to numerous false alarms. We propose **D**ynamic **D**ecomposition with **D**iffusion **R**econstruction (D$^3$R), a novel anomaly detection network for real-world unstable data to fill the gap. D$^3$R tackles the drift via decomposition and reconstruction. In the decomposition procedure, we utilize data-time mix-attention to dynamically decompose long-period multivariate time series, overcoming the limitation of the local sliding window. The information bottleneck is critical yet difficult to determine in the reconstruction procedure. To avoid retraining once the bottleneck changes, we control it externally by noise diffusion and directly reconstruct the polluted data. The whole model can be trained end-to-end. Extensive experiments on various real-world datasets demonstrate that D$^3$R significantly outperforms existing methods, with a 11% average relative improvement over the previous SOTA models.

Fixed broadband internet service can provide a stable broadband network of up to 100 megabits or even gigabits and users at home can use fixed broadband service for all kinds of internet surfing, including website and application access, watching videos, playing games, etc. Traditional maintenance for fixed broadband networks primarily uses human manual meth-ods, supplemented by some low-level semi-automation operations. Since the long processes with numerous network elements in the fixed broadband network, it is difficult for traditional operation and maintenance to support effectively with high quality. When abnormalities occur, it is quite manpower cost and time cost to monitor and locate faults. Therefore, to improve the autonomous capability of the fixed broadband network, intelligent operation and maintenance methods are necessary. First of all, a brand-new data pre-process method is proposed to detect anomalies and problems of slow access by selecting web services access commonly visited by users. Secondly, as the fixed broadband network is a multi-level and complex structure with only a small amount of anomaly sample data, we propose a multi-source joint anomaly detection model called MSJAD model on multi-dimensional features data. The model validation results on real datasets from the real fixed broadband network are state-of-the-art. The accuracy rate reaches 98 % and the recall is over 99 %. We have already begun to deploy the model on the real fixed broadband network and have achieved good feedback.