Time Resolution Changes in Time Series Forecasting I am doing time series forecasting for estimating monthly sales of certain consumer goods SKU. 

I have last 3 years of actual sales data, wherein first 2.5 years of data is monthly sales data, but last 6 months data daily sales data is available.

I can certainly add the daily data to get monthly data, but considering that total data points are then only 36 (for 3 months) and it may be slightly challenging to get various models like ARIMA, Exponential models.

Is there a way to use the daily last 6 months of data to improve the accuracy of the model?
I would do two separate analyses:


By day for six months
By month for the last 36 months (with the most recent 6 aggregated)


The 36 month analysis will be important for catching annual trends you wouldn't be able to see with the daily data, but you'll have a lot more fine-grain with the daily analysis to catch quarterly, monthly, and weekly cycles.