And the last task that we will consider is the prediction of the EEG signal. Time series in medicine are very important data, the analysis of which can solve many problems. One of them is the analysis of the EEG signal for the prediction of epileptic seizures. The example we'll look at uses the ARIMA-GARCH predictive model.

We will not delve into the operation of this model in detail but only mention that the GARCH model can capture the flat period and fluctuation period of time series.

Processing medical temporal data is no different from a normal data processing algorithm: you still need to remove noise, fill in missing data, and turn non-stationary data into stationary data.

This small section was devoted to a brief overview of various real-world problems for which, as you understand, models such as moving average, autoregressive model, and ARIMA are just a starter. In the future, these models will be modified depending on the type of problem, and sometimes they are not at all effective enough to solve certain problems.