How do you go about filling in missing data (imputing) that has both trends and seasonality? I have Q4 data but 10 days are missing in October. The missing data has no predictors either so simple Linear Regression is out of the question.

I’ve researched into the SARIMA models as well as Holt-Winters and Augmented Dickey-Fuller test but having trouble implementing these in python as I’m new to them.

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ADF is a stationarity test, not a forecasting model. R has a library called imputeTS that has a variety of methods available. Not sure what is best for Python.

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Could ask the SMEs. Considering the pandemic this is probably the best route. Could use the avg of preceding weeks, but that wouldn’t be very good.

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We can assume you have no years' data either, right? The imputed data here is going to be very questionable

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See above. Have some historic data available

I'd probably just use prophet to predict the 10 missing days. How critical are these 10 days?

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Could you share why you need to do this? I feel like a simple heuristic and some interviews with reps may both be a better fit in most cases and save you time

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