Anominer — detection of anomalies in time series

Anominer from Knowtion is a software system for the detection of anomalies in time series.

Here, anomalies include all changes in the data that occur due to disturbances, outliers, drift over time, and further external influences. Anominer detects anomalies in time series by comparing the data with a learned model that describes the normal behavior of the data.

The software employs two phases, the so called learning phase, where the model of the normal behavior is learned, and the evaluation phase, where Anominer monitors the data for changes.

Learning Phase: Anominer learns dependencies over time and over multiple sensors from input data. Learning is performed on-line without delay. The result is the model of the normal behavior of the data.

Evaluation Phase: During evaluation phase, Anominer continuously compares the received data with the model of normal behavior. This evaluation is also performed online allowing Anominer to detect and signal deviations immediately to the user. The result of evaluation is the so-called anomaly indicator, which describes the extent of deviation from the model of normal behavior. The anomaly indicator is calculated for each measurement and is immediately available.

Anominer uses standardized Restful web services for data input and configuration. The imput data consists of the measurement values and a time stamp:

Deployment scenario

Anominer is especially suitable for repeating processes, such as surveillance of machines, production processes, or test stands. Due to the continuous on-line surveillance, Anominer can be employed within scenarios that require immediate intervention in the case of an error.

Setting up Anominer is considerably simplified due to the automatic learning phase. Complex scenarios, which are difficult to monitor due to their interdependencies and cannot be surveilled with simple thresholds, can easily be monitored by evaluating the learned model.

Use Case Filling Plant

Filling plant error detection: This use case shows the deployment of Anominer for online monitoring of a filling plant. It is shown that Anominer detects changes and disturbances during the filling process immediately.

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Use Case DC Motor

DC motor error detection: This use case shows the employment of Anominer for on-line monitoring of a DC motor.

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Your benefit

Anominer is being developed as part of a ZIM cooperation project together with the Intelligent Sensor-Actuator-Systems Laboratory (ISAS) at the Karlsruhe Institute of Technology (KIT). This project is funded by the Federal Ministry of Economics and Technology based on a decision of the German Bundestag.


Knowtion was founded in 2011. Our highly qualified team members have been active for more than eight years in the research and develop- ment of algorithms for sensor fusion and automatic data analysis and have successfully used them in a variety of client projects.


With our expertise we can help you to deduce more information from your data and get a better basis for assessments and forecasts. Your product will be more reliable and accurate, so you gain a competitive advantage over your competitors.


We have profound understanding and expertise in the field of sensor fusion and automatic data analysis. We are familiar with stochastic estimation theory, information fusion, machine learning, neural network and fuzzy logic.


Knowtion UG
home An der RaumFabrik 33c
D-76227 Karlsruhe
speech+49 721 486 995-10

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