Tagged articles

time series analysis

12 articles · Page 1 of 1

Jul 30, 2026 · Artificial Intelligence

How MSH‑LLM Fuses Multi‑Scale Hypergraphs with Large Language Models for Time‑Series Analysis

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.

Large Language ModelsMSH-LLMcross-modal alignment

0 likes · 18 min read

How MSH‑LLM Fuses Multi‑Scale Hypergraphs with Large Language Models for Time‑Series Analysis

dbaplus Community

Apr 6, 2026 · Operations

How Machine Learning Transforms Database Monitoring: From Fixed Thresholds to Intelligent Anomaly Detection

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.

Alert ConvergenceDBADatabase Monitoring

0 likes · 23 min read

How Machine Learning Transforms Database Monitoring: From Fixed Thresholds to Intelligent Anomaly Detection

Bighead's Algorithm Notes

Oct 17, 2025 · Artificial Intelligence

Exploring MLLM4TS: A Universal Multimodal Framework for Time‑Series Analysis

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.

Multimodal LLMVision-Language Fusionablation study

0 likes · 11 min read

Exploring MLLM4TS: A Universal Multimodal Framework for Time‑Series Analysis

Instant Consumer Technology Team

Jul 2, 2025 · Operations

How to Build a Full‑Chain Metric Anomaly Detection Framework for Business Operations

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.

Prophet modelSHAPbusiness analytics

0 likes · 20 min read

How to Build a Full‑Chain Metric Anomaly Detection Framework for Business Operations

ITPUB

Apr 27, 2024 · Databases

How Vector Databases Enable High‑Dimensional Stock Quant Analysis

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.

AI infrastructureLanceDBhigh-dimensional data

0 likes · 7 min read

How Vector Databases Enable High‑Dimensional Stock Quant Analysis

Model Perspective

Jan 10, 2023 · Fundamentals

Fit Real-World Data with Fourier Series Using Python

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.

Pythondata fittingtime series analysis

0 likes · 6 min read

Fit Real-World Data with Fourier Series Using Python

NetEase Cloud Music Tech Team

Nov 18, 2022 · Artificial Intelligence

Machine Learning-Based Anomaly Detection for Core Business Metrics

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.

3-sigmaData IntelligenceHolt-Winters

0 likes · 11 min read

Machine Learning-Based Anomaly Detection for Core Business Metrics

dbaplus Community

Oct 21, 2022 · Databases

How Meituan Uses AI to Detect Database Anomalies in Real Time

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.

AIDatabase Anomaly Detectionreal-time monitoring

0 likes · 18 min read

How Meituan Uses AI to Detect Database Anomalies in Real Time

Meituan Technology Team

Sep 1, 2022 · Databases

AI-Powered Database Anomaly Detection Service: Feature Analysis, Algorithm Selection, and Real-Time Monitoring

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.

AI AlgorithmsBoxplotDatabase Anomaly Detection

0 likes · 19 min read

AI-Powered Database Anomaly Detection Service: Feature Analysis, Algorithm Selection, and Real-Time Monitoring

Efficient Ops

Jun 1, 2021 · Artificial Intelligence

How Time‑Series Analysis Powers AIOps: Overcoming Real‑World Challenges

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.

AIAIOpsData pipelines

0 likes · 7 min read

How Time‑Series Analysis Powers AIOps: Overcoming Real‑World Challenges

DataFunTalk

Sep 16, 2020 · Artificial Intelligence

Hotspot Mining and Event Extraction in Tencent Information Flow: Methods, Framework, and Applications

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.

Event ExtractionNLPhotspot mining

0 likes · 17 min read

Hotspot Mining and Event Extraction in Tencent Information Flow: Methods, Framework, and Applications

Baidu Tech Salon

Aug 18, 2014 · Big Data

Big Data and Prediction: Insights from Baidu Research Lab

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.

Baidumachine learningprediction

0 likes · 8 min read

Big Data and Prediction: Insights from Baidu Research Lab