Timeseer.AI
Who Is the Company Behind Timeseer.AI?
- Seller: Timeseer.AI
- Year Founded: 2020
- HQ Location: Antwerp, BE
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LinkedIn® Page: www.linkedin.com
20 employees on LinkedIn®
Total Products under this Category: 47
Last updated: September 10, 2026
Why You Can Trust G2's Software Rankings:
G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category.

Highlighted products: Nixtla, Seeq, Amazon Forecast, TrendMiner, SAP HANA Cloud, Minitab Statistical Software, Google Cloud Interference API, and InfluxDB.
Underlying data: [Grid® JSON](https://www.g2.com/categories/time-series-intelligence/grids.json?focus%5B%5D=nixtla&focus%5B%5D=seeq&focus%5B%5D=amazon-forecast&focus%5B%5D=trendminer&focus%5B%5D=sap-hana-cloud-2025-10-01&focus%5B%5D=minitab-statistical-software&focus%5B%5D=google-cloud-interference-api&focus%5B%5D=influxdata-influxdb)
VictoriaMetrics Anomaly Detection is a service that continuously scans time series stored in VictoriaMetrics and detects unexpected changes within data patterns in real time. It does so by utilizing user-configurable machine learning models. In the dynamic and complex world of system monitoring, VictoriaMetrics Anomaly Detection, a part of our Enterprise offering, is a pivotal tool for achieving advanced observability. It empowers SREs and DevOps teams by automating the intricate task of identifying abnormal behavior in time-series data. It goes beyond traditional threshold-based alerting, utilizing machine learning techniques to detect anomalies and minimize false positives, thus reducing alert fatigue. Providing simplified alerting mechanisms atop unified anomaly scores enables teams to spot and address potential issues faster, ensuring system reliability and operational efficiency.