As we have become acquainted with the main processes, now we can proceed with their forecasts. To do this, let's get to know the TimeSeriesModelFit function by considering the following example. Suppose we have data on the air temperature in London from January 1, 2012 to September 1, 2015, and we want to get the forecast for the next 6 days. Moreover, we want to compare how the forecast we receive will differ from the actual one. We will also build 95% confidence intervals that should include almost all the possible results of the forecast:
Take into account that Mathematica has reported that the SARIMA model is the most suitable one for this time series. This is true, as air temperature is highly dependent on season. Further more, in order to build a graph, we did not take all the data, but just the part from August, by cutting off all other data using the TimeSeriesWindow function. In order to build a forecast, we called the TimeSeriesForecast...