A turing test of bootstrap scenarios

Scenario generation can be an important part of the training of human decision makers. A good scenario generation method should be able to generate large numbers of realistic scenarios. When the scenarios take the form of univariate stationary time series, the moving blocks bootstrap has the potential to be a good automatic scenario generator. However, one must determine the proper bootstrap block length. We have developed a method of setting the block length based on the distribution of a statistic computed from zero crossing counts. To test whether this way of setting the block length results in realistic scenarios, we performed two Turing tests. These visualization experiments confirmed that, when a bootstrap is optimally tuned, it is difficult for sophisticated subjects to identify a bootstrap sample plotted among several real samples. The main visual defects in moving blocks bootstrap samples are sudden jumps at block boundaries and repeating patterns.