This week, we dig deeper into the ARIMA stochastic process as the second entry in our “Unplugged” (or “Under the Hood”) tutorial series. We’ve featured ARIMA models in a few of our tutorials before, but this week we’ll explore them in rich detail, starting with a clear definition of the process and moving on from there.
Why should we care? In financial time series and other fields, we often face a non-stationary time series, for example, traded security (e.g., stock, bond, commodity, etc.) prices. In this case, the time series exhibits either trending, seasonality, or merely misguided (random) walk. The stationarity assumption is essential in econometric modeling, so how do we handle this scenario? ARIMA.
Once again, we will start here with the process definition, stating the inputs, outputs, parameters, stability constraints, and assumptions. Then we will introduce the integration operator and draw a few guidelines for the modeling process.