As suggested by Tim, gung, whuber I am editing this question and narrowing down the problem.
For hotels, I want to forecast number of room bookings that will happen x days before the day of check-in. So my data will be
x days before check-in
what happened in past, x days before the day of check-in.
air traffic data, to test if it has an impact.
What I need help for is: For demand forecasting purpose, is there any methodology which is 'one size fits all' type and has following features.
Creates a model by itself when fed input data and determines mathematical equation or learns from data.
Works for any and every situation. To give context a client in Europe might have same data structure in their sql database in terms of name, variable type in comparison to sql database of a client who is based in Singapore. However, Singapore data might have different trend, seasonality and pattern in comparison to data in Europe. Is there any technique which can work in all such situations.
It adjusts to new trends/patterns that appear over time which might not have been captured in the existing model.