How to use Kernel Density Estimation for Prediction? I would like to apply KDE to inventory replenishment, but I am not sure how to use the analysis to predict future sales based on past sales. Given a set of data and having applied KDE to it (probably using a Gaussian distribution), how do I make a prediction about the future? 
Thanks for any help! Please let me know if I can clarify - I'm only starting to pick up the language for talking about KDE ... I'm glad to do reading on my own - pointers to any resources would be welcome.
 A: You can use conditional kernel density estimation to obtain the density of sales at time $t+h$ conditional on the values of sales at times $t, t-1, t-2, \dots$ This gives you a density forecast rather than a point forecast. The problem is that the conditioning is difficult in a density setting when the number of conditioning variables is more than 2. See this paper for a discussion of the basic idea.
An alternative procedure that imposes more assumptions (but allows more conditioning variables) is to fit an additive autoregression such as described in Chen and Tsay (1993) and then use kde on the residuals to obtain the forecast densities.
However, I suspect that both of these are more complicated than what you really want. I suggest you read a textbook on demand forecasting such as Levenbach and Cleary (2006).
A: I would have thought that KDE bear little if any relationship to predicting future sales based on past sales. Sounds more like time series analysis to me, though that's really not my area.
