ARIMA
Earlier, in the section on exploratory data analysis, we talked about how seasonality and stationarity are important elements when it comes to forecasting time series. In fact, median forecasting has trouble with both. If the mean of a time series continuously shifts, then median forecasting will not continue the trend, and if a time series shows cyclical behavior, then the median will not continue with the cycle.
ARIMA which stands for Autoregressive Integrated Moving Average, is made up of three core components:
Autoregression: The model uses the relationship between a value and a number of lagged observations.
Integrated: The model uses the difference between raw observations to make the time series stationary. A time series going continuously upward will have a flat integral as the differences between points are always the same.
Moving Average: The model uses residual errors from a moving average.
We have to manually specify how many lagged observations we want to include, p, how...
