Building a time-series demand forecasting model

I am forecasting demand for certain types of goods and services, which I expect to be correlated to a sub-set of a basket of macroeconomic indicators (considering 15-20 indicators)

I do not know which indicators influence the demand more, whether they have a simple correlation influence or whether a derivative of change influences the demand (i.e. GDP or GDP change for example) or whether there’s a delayed effect on demand (e.g. increased government spend in last year may better predict this year’s demand?). Some macro indicators may be correlated to each other.

I have some basic hypotheses on likely indicators - that may be right or wrong.

Questions 1. What are good time-series forecasting models? What can be considered, apart from just a multivariate regression? 2. Is there a tool whereby I can input the historical demand, historical macro indicators, which will then output which set of indicators best predict the demand and which model works best?

I know how to do regressions in excel, but that’s just one set of indicators at a time. 20 indicators (plus derivatives, plus lag) throws up so many possibilities I cannot manually simulate.

Any help appreciated.

It doesn't seem like your problem is related to the time series nature of the problem. It seems like your problem is that you have "too many" possible independent variables, and the "kitchen sink" approach of regressing on all of them is creating multi-colinearity or very low predictive ability, say as measured by $$r^2$$.