Forecasting from time series data using ARIMA

In the previous recipe, we generated a model for a stationary time series using an ARMA model, which consists of an AR component and an MA component. Unfortunately, this model cannot accommodate time series that have some underlying trend; that is, they are not stationary time series. We can often get around this by differencing the observed time series one or more times until we obtain a stationary time series that can be modeled using ARMA. The incorporation of differencing into an ARMA model is called an ARIMA model.

Differencing is the process of computing the difference between consecutive terms in a sequence of data – so, applying first-order differencing amounts to subtracting the value at the current step from the value at the next step (). This has the effect of removing the underlying upward or downward linear trend from the data. This helps to reduce an arbitrary time series to a stationary time series that can be modeled...