Chapter 9 Conventional ARIMA
Another important dynamic element in ADAM is ARIMA model (developed originally by Box and Jenkins 1976). ARIMA stands for "AutoRegressive Integrated Moving Average", although the name does not tell much on its own and needs additional explanation, which will be provided in the next sections.
The main idea of the model is that the data might have dynamic relations over time, where the new values depend on the values on the previous observations. This becomes more obvious in case of engineering systems and modelling phisical processes. For example, Box and Jenkins (1976) give an example of a series of CO\(_2\) output of a furnace, when the input gas rate changes. In this case, the elements of ARIMA process are natural, as the CO\(_2\) cannot just drop to zero, when the gas is switched off - it will leave the furnace, in reducing quantity over time (i.e. leaving \(\phi_1\times100\%\) of CO\(_2\) in the next minute, where \(\phi_1\) is a parameter in the model).
Another example, where AR processes are natural is the temperature in the room, measured with 5 minutes intervals. In this case the temperature at 5:30pm will depend on the one at 5:25pm (if the temperature outside the room is lower, then it will go down slightly due to the loss of heat). So, in these examples, ARIMA model can be considered as a true model, but when it comes to time series from the social or business domain, it becomes very difficult to motivate the usage of ARIMA from the from the modelling point of view. For example, the demand on products does not reproduce itself and in real live does not depend on the demand on previous observations, unless we are talking about a repetitive purchases by the same group of consumers. So, if we construct ARIMA for such process, we are closing eyes on the fact that the observed time series relations in the data are most probably spurious. At best, ARIMA in this case can be considered as a very crude approximation of a complex true process (demand is typically influenced by price changes, consumer behaviour and promotional activities). Thus, whenever we work with ARIMA models in social or business domain, we should keep in mind that they are wrong even from the philosophical point of view. Nevertheless, they still can be useful, which is why we discuss them in this chapter.
Note that this is a heavy mathematical chapter, and here we will discuss the main theoretical properties of ARIMA processes (i.e. what would happen if the data indeed followed the specified model), moving to more practical aspects in the next chapter.
Box, George, and Gwilym Jenkins. 1976. Time series analysis: forecasting and control. Holden-day, Oakland, California.