The abbreviation ARIMA stands for AutoRegressive Integrated Moving Average and refers to a class of statistical models used to analyze time series data. This model can be used to make predictions about the future development of data, for example in the scientific or technical field. The ARIMA method is primarily used when there is a so-called temporal autocorrelation, i.e. simply put, the time series shows a trend.

In this article, we will explain all aspects related to ARIMA models, starting with a simple introduction to time series data and its special features, until we train our own model in Python and evaluate it in detail at the end of the article.

What is time series data?

Time series data is a special form of dataset in which the measurement has taken place at regular, temporal intervals. This gives such a data collection an additional dimension that is missing in other datasets, namely the temporal component. Time series data is used, for example, in the financial and economic sector or in the natural sciences when the change in a system over time is measured.