Fine-tuning your AutoML forecasting model
In this section, you will first review tips and tricks for improving your AutoML forecasting models and then review the algorithms used by AutoML for forecasting.
Improving AutoML forecasting models
Forecasting is very easy to get wrong. It's easy to produce a model that seems to work in development, but fails to make accurate predictions once deployed to production. Many data scientists, even experienced ones, make mistakes. While AutoML will help you avoid some of the common mistakes, there are others that require you to exercise caution. In order to sidestep these pitfalls and make the best models possible, follow these tips and tricks:
- Any feature column that you train with has to be available in the future when you make a prediction. With
OJ Sales Sample, this means that, if you want to predict the quantity of sales 6 weeks out and include price as an input variable, you need to know the price of each product 6 weeks...
