# Would you pay for moving average?

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).

Python motto.Batteries Included.

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.