Time Series Forecasting in Python

7 Forecasting non-stationary time series

This chapter covers

• Examining the autoregressive integrated moving average model, or ARIMA(p,d,q)
• Applying the general modeling procedure for non-stationary time series
• Forecasting using the ARIMA(p,d,q) model

In chapters 4, 5, and 6 we covered the moving average model, MA(q)); the autoregressive model, AR(p)); and the ARMA model, ARMA(p,q). We saw how these models can only be used for stationary time series, which required us to apply transformations, mainly differencing, and test for stationarity using the ADF test. In the examples that we covered, the forecasts from each model returned differenced values, which required us to reverse this transformation in order to bring the values back to the scale ...