How do you predict ARIMA?

1- Check again the stationarity of the time series using augmented Dickey-Fuller (ADF) test . 2- Try to increase the number of predictors ( independent variables). 3- Try to increase the sample size (in case of monthly data, to use at least 4 years data.

Python predict() function enables us to predict the labels of the data values on the basis of the trained model. The predict() function accepts only a single argument which is usually the data to be tested.

ARIMA, short for ‘AutoRegressive Integrated Moving Average’, is a forecasting algorithm based on the idea that the information in the past values of the time series can alone be used to predict the future values.

Example:

STEPS