I have sales training data from
2020-06 and I have to predict sales from
2020-06 to today. The pandemic had a great impact on sales. Are there methods to make my ML forecasting models more robust? Here are some ideas:
- Add a binary variable,
pandemic, being 1 when
date > 2020-03and 0 otherwise.
- Alternatively, define two variables
soft_pandemicdescribing respectively the data between March/April 2020 and data after May 2020.
- Weigh the pandemic data more, since they describe better the current situation.
Are there drawbacks to these methods? Are there alternative ways to improve this?