I'm going through Google's Unnoficial Data Science blog, specifically Our quest for robust time series forecasting at scale.
Their approach to forecasting includes making weekly total forecasts, and then distributing those totals to the individual days of each week. I'm wondering what is the recommended way to do this.
Specifically, if I have a time series of daily/hourly data, aggregate it as weekly totals, and then make forecasts for the following weeks, how can I distribute those totals to daily/hourly values?
One approach I've considered is somehow applying Rob Hyndman's advice from Forecasting: Principles and Practice on Ensuring forecasts stay within limits, but it feels like I'm overcomplicating the problem.
Edit: I've found several references for using optimal forecast reconciliation to adjust the forecasts for both the aggregated data and the individual values. Still trying to determine whether it can be used to only reconcile the daily/hourly values, based on the weekly totals.