Many customers are asking THE question: which forecasting models are you using? Indeed, our technology page isn’t very specific on the subject.
Disclaimer: I am not really going to answer this question in this post, so please, don’t be too disappointed.
Actually, there are two main reasons why we do not disclose this information
- it’s a proprietary technology (like Google search).
- it’s a super counter-intuitive technology.
Yet, in order to clarify the situation, I can say that Lokad is not using any silver-bullet forecasting model (i.e. a super-model that would fit all situations), but tons of models instead.
For example, we do use simple moving average (among others naturally) which is probably the most naive forecasting method. Intuitively, simple moving average says: if you want to know the total sales next month, just take the average monthly sales over the last 6 months.
In the first sight, it might appear shocking to sell forecasts, if, in the end, it’s moving average model that gets used. But, in my opinion, it is not.
Indeed, producing forecasts through a statistical model is only the last step of a complicated process. Before that, you need to choose the model to be used. And, this step is very complicated.
Thus, Lokad can indeed produce a forecast based on a moving average model, if we detect the moving average model as being the best available model for this particular situation (in practice, this situation arises for very short or very erratic time-series).
Batteries Included. Python motto.
But the key difficulty of the problem is to understand why the moving average model has been selected. With regular statistical packages, choosing the right model is the user’s burden. With Lokad, it’s part of the service.
Ps: there are more complex variant of the moving average where decreasing coefficients (also called weights) are applied to the time-series; but it’s beyond the scope of the discussion.