Last time we have combined AR(p) and MA(q) models to make ARMA(p, q) model. We are getting quite close to understanding what ARFIMA processes are, but to do that we still have things to look at. This time lets take into account letter I (which stands for "integrated") - lets look at ARIMA models.

In this Numberphile video author and economist Tim Harford talks about where the babies come from and to what conclusions bad statistics can lead us to.

In the last few posts we have talked about how physical models relate to "economical" AR(p) models. Yet our end goal is to about ARFIMA models of which "AR" makes up just about 1/3rd. In this post we introduce you to MA(q) models and show how they combine AR(p) models to make up ARMA(p, q) models.

In the last few posts we have seen that random walk can be written in recursive form, which suggests that random walk is AR(1) process. We have also became familiar with the partial auto-correlation functions. Here in this post we show that PACF can provide an intuition on the order of AR which should be used in modeling the data.

In the last post we have seen that auto-correlation function breaks when try to analyze random walk time series. We have used differencing technique which has allowed us to circumvent non-stationarity of the random walk series.

In the upcoming post in our ARFIMA series we will use another technique known as partial auto-correlation function (abbr., PACF). This new technique is discussed by ritvikmath in the video below. Watch in order to understand the new tool.