Time Series Analysis

Analyzing development over time


Identifying structures

The aim of time series analysis is to identify patterns and regularities in the development of measurement signals over time with the objective of a better understanding of the processes that the data is based upon, recognizing anomalous behavior, and predicting the time sequence of signals. Since the measurements of a time series are influenced by many factors, many of which are unknown or whose effects are too complex, stochastic models are used to model the time sequence of the signals.


Time series analysis methods

There are many different methods for analyzing time series. These include smoothing processes which replace random measurements of a signal with corrected approximations. With the Fourier transform, the time series is broken down into its frequency spectrum, which can be investigated to see which frequency components the signal contains. With the help of regression methods it is possible to determine time trends and seasonal influences and with recurrent neural networks it is possible to predict the development of non-linear dynamic systems over time.     

Determining the optimum maintenance time

In addition to real-time anomaly recognition in sensor data, long-term prediction of the development of relevant key variables over time also plays a key role. Key variables could be, for example, a machine’s performance or its oil quality. If the development of these key variables over time can be predicted with sufficient accuracy, spare parts can be ordered on time and service technicians can be scheduled for maintenance. This helps to prevent costly downtimes.

Prediction approaches based on a linear extrapolation of measured variables can lead to highly inaccurate predictions. The approaches developed by us are based on machine learning approaches. They offer improved predictions with additional information regarding the uncertainty of the prediction.

Your benefit from the time series analysis

Recognize errors
in good time

Reduce
downtime

reduce
maintenance costs

Develop
smart services

Any questions? We are happy to help.

Katana, the data analytics segment of the USU Group, has extensive experience in the application of machine learning methods in the industrial sector. Take advantage of our knowledge and our solutions for building your data-driven business models to reduce your costs and improve your quality and value added.