Submitted by PM_ME_YOUR_GIGI t3_10iktr2 in MachineLearning
Hi guys! I'm currently trying to forecast a product's demand for the upcoming months (March and April). I have data relating to this product's demand since January 1999. However, the COVID-19 pandemic greatly disrupted the time series' patterns for 2020 and 2021. How should I deal with data from March 2020 to around Jan 2022?
Should I completely discard it and only include data from Jan 1999 to Dec 2019, and then Jan 2022 onwards? I'm struggling to find any good articles on how predictive tasks are now being conducted. Are there papers that suggest particular "denoising" techniques for pandemic data?
Thank you!
DW_Dreamcatcher t1_j5hsbep wrote
Try models that exclude it, include it, and try to compare potential variance 2022 onwards. You’re right that pandemic prediction is out of scope, but assessing variation and noise is a great way to show maturity to your company. :)