TITLE: What is the most scientific way to assign weights to historical data?
QUESTION [6 upvotes]: This is a common question I usually face while processing historical data. I have year on year data of an event for the past N years.I would like to assign weights to the data of these N years so that the data corresponding to the most recent year as the highest weight and the data corresponding to the oldest year has the least. This is to give more importance to recent trend as compared to very old trends.
Questions

Is there any scientific way to assign weights to the years i.e. what should be the weight for the most recent year, what should be the weight for the previous year an so on? 
Is there any scientific justification in assigning equal weights to the preceding historical data after a certain point?
Is there any scientific justification to ignore the preceding historical data after a certain point i.e. weight = 0?

I think the answers will depend on the exact nature of the data under study and we might not have a general answer that will work with all data. Even then can we at least have a rule of thumb which is independent of the actual data. Any reference or pointers to literature would also be helpful.

REPLY [3 votes]: Michael's ideas sound great. If you are able to frame your problem as a prediction problem, there is an empirical way to make this choice between Michael's suggestions.
In the context of machine learning, we are talking about solving a supervised learning problem where the optimal choice can be done using cross validation, given sufficient historical data. E.g. the method (1, 2, or 3) and its parameters (e.g. $n$ years) can be selected such that expected predictive performance is maximized on a holdout set. Be careful so as to evaluate performance on future holdout windows. "Time series cross validation" is easy to do wrong.