The precise and large dataset concerning catastrophic events is very important for insurers. To improve the quality of such data three methods based on the bootstrap, bootknife, and GAN algorithms are proposed. Using numerical experiments and real-life data, simulated outputs for these approaches are compared based on the mean squared (MSE) and mean absolute errors (MAE). Then, a direct algorithm to construct a fuzzy expert's opinion concerning such outputs is also considered.

翻译：关于巨灾事件的精确且大规模数据集对保险公司至关重要。为提高此类数据质量，本文提出了基于自助法、刀切法和生成对抗网络算法的三种方法。通过数值实验和实际数据，基于均方误差和平均绝对误差对这些方法的模拟输出进行了比较。此外，本文还提出了一种直接算法，用于构建专家对此类输出的模糊意见。