Scenario generation by selection from historical data
Peer reviewed, Journal article
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Original versionComputational Management Science. 2021, 18 411-429. 10.1007/s10287-021-00399-4
In this paper, we present and compare several methods for generating scenarios for stochastic-programming models by direct selection from historical data. The methods rangefromstandardsamplingandk-means,throughiterativesampling-basedselection methods, to a new moment-based optimization approach. We compare the models on a simple portfolio-optimization model and show how to use them in a situation when we are selecting whole sequences from the data, instead of single data points.