I am looking for information regarding uncertainty handling. Specifically, I am interested in finding information related to sampling techniques and scenario generation. I am very interested in finding new techniques of which I am not familiar with, and also scientific articles which perhaps investigate the different techniques.
More about the problem. I am currently working with time series data where I in an optimization setting have to handle the uncertainty of e.g. predicting the response variable. So far, the methods that I know are limited to scenario generation and Monte Carlo simulation.
Scenario generation: Predict future values using e.g. ARIMA models. Generate several observations for each time point. Use clustering to generate scenarios and probabilities of scenarios.
Monte Carlo: Sample time points from known data and hereafter feed this to the optimization problem.
Any ideas for other techniques (perhaps even just the names) or articles would be a big help.
Thank you!