In this webinar hosted by InfluxDB and HiveMQ, we focus on how you can create value for your business using new tools in the AI and database ecosystem to quickly deploy AI models to perform tasks like anomaly detection. The webinar starts with a high-level overview of how MQTT and time series data can be valuable in an industrial IoT environment.

Welcome to the third chapter of the handbook on Anomaly Detection for Time Series Data! This series of blog posts aims to provide an in-depth look into the fundamentals of anomaly detection and root cause analysis. It will also address the challenges posed by the time-series characteristics of the data and demystify technical jargon by breaking it down into easily understandable language.

Any existing InfluxDB user will notice that InfluxDB underwent a transformation with the release of InfluxDB 3.0. InfluxDB v3 provides 45x better write throughput and has 5-25x faster queries compared to previous versions of InfluxDB.

We’re extremely excited to announce the availability of the EventSentry v5.1, which will detect threats and suspicious behavior more effectively – while also providing users with additional reports and dashboards for CMMC and TISAX compliance. The usability of EventSentry was also improved across the board, making it easier to use, manage and maintain EventSentry on a day-by-day basis. We also released 60+ training videos to help you get started and take EventSentry to the next level.

Welcome to the second chapter of the handbook on Anomaly Detection for Time Series Data! This series of blog posts aims to provide an in-depth look into the fundamentals of anomaly detection and root cause analysis. It will also address the challenges posed by the time-series characteristics of the data and demystify technical jargon by breaking it down into easily understandable language. This blog post (Chapter 2) is focused on different types of anomalies.

Welcome to the handbook on Anomaly Detection for Time Series Data! This series of blog posts aims to provide an in-depth look into the fundamentals of anomaly detection and root cause analysis. It will also address the challenges posed by the time-series characteristics of the data and demystify technical jargon by breaking it down into easily understandable language. This blog post (Chapter 1) is focused on.

We have recently added a more detailed anomaly rate chart to Netdata that breaks out the overall node anomaly rate by type, this lets you more easily see what parts of your infrastructure might be experiencing an uptick in anomalies when you see the overall node anomaly rate increase.

Hey there, data detectives and server sleuths! Ever find yourself staring at a screen full of numbers and graphs, only to have one data point wave at you like a tourist lost in Times Square? Yup, you’ve stumbled upon the cheeky world of Anomaly Detection—where data points act more mysterious than your cat when it suddenly decides to sprint around the house at 2 AM. So buckle up!

Anomaly detection is one of the most common problems that Splunk users are interested in solving via machine learning. This is highly intuitive, as one of the main reasons our Splunk customers are ingesting, indexing, and searching their systems’ logs and metrics is to find problems in their systems, either before, during, or after the problem takes place. In particular, one of the types of anomaly detection that our customers are interested in is time series anomaly detection.