Say I have a years worth of data on donut sales, and I use linear regression with variables such as daily occupancy, daily weather etc. because there is a good correlation between each variable and donut sales. As I am developing this model I find better variables and get a more accurate representation, which is noted by the increased adjusted R-squared value.
Would me improving this model be the best way to improve the forecast for projected sales? I want to very accurately predict my sales over the next year.
I thought the method was a good way to forecast, but I am starting to have doubts because although I am putting much time trying to essentially improve the fit of old data, since forecasting is more random am I just wasting time?
Are there any tips for better forecasting techniques other than linear regression? Thanks