This article details a practical roadmap for building production-grade fault prediction systems at massive scale, covering target selection, data governance, model evolution, time-window design, and safe action loops—emphasizing that reliable prediction requires engineering rigor beyond just model training.
The paper introduces MSH‑LLM, a multi‑scale hypergraph framework that aligns natural language and time‑series modalities via a cross‑modal alignment module and mixed prompts, achieving state‑of‑the‑art performance on 27 real‑world datasets across forecasting, classification, few‑shot and zero‑shot tasks.
This article explains why traditional threshold‑based database inspections are insufficient, introduces machine‑learning‑driven anomaly detection as a second set of eyes, details feature extraction, algorithm choices, tuning, and alert convergence, and showcases three real‑world scenarios with MySQL and Redis metrics.
This article reviews the MLLM4TS framework, which fuses visual representations of multivariate time series with large language models to address complex temporal dependencies, cross‑channel interactions, and task generalization, and demonstrates superior performance on classification, anomaly detection, forecasting, and few‑shot scenarios across multiple benchmarks.
This article explains how to design a complete metric‑abnormality pipeline—from real‑time threshold alerts and statistical tests such as 3σ, GESD, IQR, and MBP to trend analysis with Mann‑Kendall and Prophet, and finally to deterministic and probabilistic attribution using contribution decomposition and SHAP, all illustrated with practical business cases.
This interview‑style guide explores how vector databases handle massive, high‑dimensional time‑series data for quantitative stock trading, detailing data scaling challenges, selection criteria, and why the research team chose LanceDB over alternatives for efficient, scalable financial analysis.
This article explains Fourier series theory, demonstrates how to remove linear trends from monthly CO₂ data, and shows step‑by‑step Python code using SciPy's curve_fit to fit and predict the data with a 100‑term Fourier expansion.
The paper proposes a containerized, machine‑learning framework that fuses rule‑based and XGBoost‑driven anomaly detection to monitor daily active users on a cloud music platform, achieving 89 % recall, 81 % precision and up to 74 % recall improvement over traditional threshold methods, while outlining future model refinement and broader metric applicability.
Meituan's database platform team built an AI‑driven anomaly detection service that automatically extracts feature patterns, selects appropriate statistical algorithms, trains models, and performs both offline and online monitoring to quickly locate and mitigate database issues across diverse production scenarios.
The article details Meituan's database platform team's end‑to‑end design of an AI‑driven anomaly detection service, covering feature analysis of time‑series patterns, algorithm selection (MAD, boxplot, EVT), model training, real‑time detection with Flink, operational metrics, and future enhancements.
At the 16th GOPS Global Operations Conference, Shen Hui of DingMao Technology explained how time‑series data analysis underpins AIOps, outlining its four‑step workflow, key challenges, and the company’s three‑pipeline solution that enables trend forecasting, fault prediction, and a robust AI‑driven operational platform.
This article presents Tencent's research on hotspot mining and event extraction for information flow, detailing the challenges of timeliness, comprehensiveness, and heat rationality, the combined use of time‑series analysis, topic detection, clustering, and dynamic‑time‑warping, and the resulting framework and its applications to text, image, and video recommendation.
At Baidu’s 53rd Technology Salon, researcher Shen Zhiyong outlined the lab’s vision of an online intelligent system that unifies monitoring, anomaly detection, diagnosis and big‑data‑driven prediction—using time‑series, causal and simulation analyses—to forecast tourism crowds, predict Gaokao essay topics, and illustrate both the opportunities and challenges of processing massive, heterogeneous data for real‑time decision support.
