Fundamental Flaw of the Box-Jenkins Methodology

If you have taken a course on forecasting or time series analysis, you’ve probably heard of ARIMA and the Box–Jenkins methodology. In my opinion, this methodology has a fundamental flaw and should not be used in practice. Here’s why. When Box and Jenkins wrote their book back in the 1960s, it was a very different […]

Why is it hard to beat the Simple Moving Average?

Simple Moving Average (SMA) is one of the basic forecasting methods. It doesn’t rely on time series decomposition, doesn’t have a seasonal component, and doesn’t include explanatory variables. Yet, in a supply chain context, SMA is sometimes a tough benchmark to beat. Why? First things first, SMA is simply the arithmetic mean of several recent […]

What about the training/test sets?

Another question my students sometimes ask is how to define the sizes for the training and test sets in a forecasting experiment. If you’ve done data mining or machine learning, you’re likely familiar with this concept. But when it comes to forecasting, there are a few nuances. Let’s discuss. First and foremost, in forecasting, the […]

How to choose forecast horizon?

One of the questions my students sometimes ask is how to set the forecast horizon. The answer depends largely on the task at hand, but there are still some guidelines. First, the forecast horizon depends on data granularity. A “year ahead” forecast on monthly data means forecasting 12 steps ahead, while for daily data, it […]