Forecasting univariate time series data with non-seasonal ARIMA
In this recipe, you will explore non-seasonal ARIMA and use the implementation in the statsmodels package. ARIMA stands for Autoregressive Integrated Moving Average, which combines three main components: the autoregressive or AR(p) model, the moving average or MA(q) model, and an integrated (differencing) factor or I(d).
An ARIMA model can be defined by the p, d, and q parameters, so for a non-seasonal time series, it is described as ARIMA(p, d, q). The p and q parameters are called orders; for example, in AR of order p and MA of order q. They can also be called lags since they represent the number of periods we need to lag for. You may also come across another reference for p and q, namely polynomial degree.
ARIMA models can handle non-stationary time series data through differencing, a time series transformation technique, to make a non-stationary time series stationary. The integration or order of differencing...
