VictoriaMetrics Observability Blog

Fred Navruzov
Category
Clear all filters

Filter: Fred Navruzov

What's new in VictoriaMetrics Anomaly Detection (Q2 2026)

The Q2 2026 vmanomaly update introduces Temporal Envelope, a redesigned UI, faster online-model execution, and an AI-assisted workflow that turns natural-language monitoring goals into tested configurations and alerting rules.

What's new in VictoriaMetrics Anomaly Detection (Q1 2026)

Q1 2026 brought incremental but important updates to VictoriaMetrics Anomaly Detection: UI improvements, AI assistance inside the UI, a public traces playground, new false-positive reduction controls, and continued resource optimizations.

What’s new in VictoriaMetrics Anomaly Detection (2025)

VictoriaMetrics Anomaly Detection has had a productive year with lots of user feedback that has had a major impact on product development. We’ve added improvements across the board: in core functionality, simplicity, performance, visualisation and AI integration. In addition to bug fixes and speedups, below is a list of what was accomplished in 2025.

How a US Software Provider Improved Traffic Alerting with VictoriaMetrics Anomaly Detection

VictoriaMetrics Anomaly Detection enables reliable alerting for highly variable, multi-domain traffic without relying on static thresholds. In this case study, fine-tuned models, backtesting, and clear visualization helped reduce alert noise, improve confidence in anomaly detection, and lower operational overhead.

VictoriaMetrics Anomaly Detection: What's New in Q3 2024?

Explore the latest improvements in VictoriaMetrics Anomaly Detection (vmanomaly), including optimizations, online models, multitenantcy and mTLS support.

VictoriaMetrics Anomaly Detection: What's New in H1 2024?

Explore the latest improvements in VictoriaMetrics Anomaly Detection (vmanomaly), including presets, new models, enhanced tuning, and better resource management

Anomaly Detection for Time Series Data: Techniques and Models

This blog post series centers on Anomaly Detection (AD) and Root Cause Analysis (RCA) within time-series data. In Chapter 3, we delve into a variety of advanced anomaly detection techniques, encompassing supervised, semi-supervised, and unsupervised approaches, each tailored to different data scenarios and challenges in time-series analysis.

Anomaly Detection for Time Series Data: Anomaly Types

This blog post series centers on Anomaly Detection (AD) and Root Cause Analysis (RCA) within time-series data. In this second part, we explore the distinct anomaly types inherent to time-series and offer insights on how to tackle them effectively.

Anomaly Detection for Time Series Data: An Introduction

This blog post series focuses on Anomaly Detection (AD) and Root Cause Analysis (RCA) within the context of time-series data. The inaugural chapter lays the groundwork by introducing the role of AD in end-to-end observability systems, discussing domain-specific terminology, and addressing the challenges inherent to the time-series nature of the data.