Let's say we want to forecast revenue by month for the next 12 months, and we have daily revenue data for the last 3 years.
We could then group this data by month, train our model using revenue by month, and forecast for the next 12 months. Alternatively, we could train our model using daily revenue data, forecast for the next 365 days and group the predictions by month to obtain a revenue by month forecast.
Which one generates more accurate results, performance notwithstanding? I wonder if there is a definitive, general answer to this.