A time series is the term used to describe observed data points that occur continuously at regular intervals. If one wants to analyze these data, for example to make a prediction about the future course, the basic approach is to find a model that describes the past data as well as possible.
In time series analysis, we humans often develop an intuitive feeling for the underlying observation and can quickly answer whether there are any anomalies or how the time series will develop further.
For an artificial intelligence (AI), however, this is not always an easy task. In addition to the selection of the mathematical model, it is also true that any AI is only as good as the data provided. To create meaningful predictive models, for example, questions have to be answered such as how domain knowledge and expert knowledge or trends and seasonalities can be integrated into the model. The Fraunhofer IOSB is researching these questions in several projects.