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I have sales training data from 2019-06 to 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 > 2020-03 and 0 otherwise.
  • Alternatively, define two variables hard_pandemic and soft_pandemic describing 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?

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