Early Anomaly Detection (AD) in sensor-based Multivariate Time Series (MTS) is crucial for addressing signs of operational failures. However, existing AD methods either struggle to identify anomalies at an early stage or lean heavily on intricate neural networks and extensive data for model training, compromising clarity and interpretability. To bridge this gap, we pioneered CAD, a novel AD framework based on correlation analysis. It harnesses Time-Series Graphs (TSGs) to monitor sensor correlation changes. By meticulously analyzing these changes, CAD excels in ascertaining the precise time of anomalies and identifying the implicated sensors. In this demonstration, we introduce EADS, an Early Anomaly Detection System built upon CAD for sensor-based MTS. We navigate multiple scenarios to illustrate the prowess of EADS in serving as an early AD benchmark platform, offering insightful abnormal time interpretability, and facilitating timely predictive maintenance. The source code is available at https://github.com/YihaoAng/EADS/.

Time Series Generation (TSG) is essential in many industries for generating synthetic data that mirrors real-world characteristics. TSGBench has advanced the field by offering comprehensive evaluations and unique insights for selecting suitable TSG methods. However, translating these advancements to industry applications is hindered by a cognitive gap among professionals and the absence of a dynamic platform for method comparison and evaluation. To address these issues, we introduce TSGAssist, an interactive assistant that integrates the strengths of TSGBench and harnesses Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) for TSG recommendations and benchmarking. Our demonstration highlights its effectiveness in (1) enhancing TSG understanding, (2) providing industry-specific recommendations, and (3) offering a comprehensive benchmarking platform, illustrating its potential to ease industry professionals' navigation through the TSG landscape and encourage broader application across industries.

Early detection of anomalies from sensor-based Multivariate Time Series (MTS) is vital for timely response to the signs of operation failures and errors. While many interesting works have been done toward solving this problem, existing methods typically detect such anomalies as outliers by making certain assumptions that allow efficient and easily understandable solutions to be used but might not be applicable to time series. Meanwhile, unsupervised deep learning-based methods might be highly accurate but often lead to challenges for real-time industrial scenarios, e.g., requiring a large amount of training data and producing unstable output.In this paper, we propose a new approach, CAD, to detect anomalies from sensor-based MTS. We aim to leverage the latent correlations between sensors by first converting the MTS into a sequence of Time-Series Graphs (TSGs) that connect sensors to their highly correlated neighbors within a certain time period. Then, we track the unusual correlation variations between sensors on the sequence of TSGs. By analyzing the correlation variations with a theoretical guarantee, CAD can detect the time of occurrence for the anomalies simultaneously with the sensors that are affected as early as possible.Extensive experiments over eight real-world datasets show that CAD is effective, scalable, yet stable compared to nine state-of-the-art methods while keeping comparable efficiency. Moreover, it maintains above 85% accuracy on large-scale datasets with over 1,000 sensors. Notably, CAD can determine relevant sensors in a very early stage of the anomaly so that timely predictive maintenance can be done. The code is available at https://github.com/YihaoAng/CAD.

Synthetic Time Series Generation (TSG) is crucial in a range of applications, including data augmentation, anomaly detection, and privacy preservation. Although significant strides have been made in this field, existing methods exhibit three key limitations: (1) They often benchmark against similar model types, constraining a holistic view of performance capabilities. (2) The use of specialized synthetic and private datasets introduces biases and hampers generalizability. (3) Ambiguous evaluation measures, often tied to custom networks or downstream tasks, hinder consistent and fair comparison. To overcome these limitations, we introduce TSGBench, the inaugural Time Series Generation Benchmark, designed for a unified and comprehensive assessment of TSG methods. It comprises three modules: (1) a curated collection of publicly available, real-world datasets tailored for TSG, together with a standardized preprocessing pipeline; (2) a comprehensive evaluation measures suite including vanilla measures, new distance-based assessments, and visualization tools; (3) a pioneering generalization test rooted in Domain Adaptation (DA), compatible with all methods. We have conducted comprehensive experiments using TSGBench across a spectrum of ten real-world datasets from diverse domains, utilizing ten advanced TSG methods and twelve evaluation measures. The results highlight the reliability and efficacy of TSGBench in evaluating TSG methods. Crucially, TSGBench delivers a statistical analysis of the performance rankings of these methods, illuminating their varying performance across different datasets and measures and offering nuanced insights into the effectiveness of each method.