# Keeping track of errors to improve later on

Lately a couple of customers have been asking whether Lokad was keeping track of its past forecast errors in order to improve its future forecasts.

The answer is simple: **yes, we do**, but there are more than that. In particular, **we do not wait for**

- the forecasts to be requested,
- the course of events to happen,
- the historical data to be updated,

to finally compare our *past* forecasts with what really happened. Indeed, such an approach would be way too slow and inefficient.

Instead, we are using cross-validation methods adapted for the purpose of time-series forecasting.The process is more simple than it sounds, let’s start with an example.

Let assume that we have a single time-series worth 1 year of weekly sales data (i.e 52 points). We want to produce 4-weeks sales forecasts - but we also **want to estimate the forecasting error**.

- take the N first points (with N = 10 initially).
- create a forecasting model based on those N points.
- create a 4-weeks ahead forecast based on this model.
- compare the forecast with the complete series.
- increment N of 1 point (i.e. 1 week).
*repeat*.

With cross-validation, we can accurately **estimate the expected forecast error** of a forecasting model. In particular, if you have two different models, cross-validation can help you choosing the best one (*). Cross-validation can also be used to **adjust model parameters** - in order to find the parameters that best fit the data.

The Lokad team continuously monitors accuracy on delivered forecasts with such cross-validation methods and keeps working on more accurate forecasting models. Thus, **we do keep track of our forecast errors, but without waiting for them to happen**.

(*) If you try too many models, then you are likely to end-up with overfitting issues, but this problem is beyond the scope of this post.