让下一篇重要论文，不再错过。

原文摘要

We present scentree, an open-source Python package for constructing scenario trees for multistage stochastic programming models using either historical data or samples drawn from a probability distribution. It combines machine learning and multivariate time series models to obtain a scenario fan that captures dependencies in the stochastic processes. This scenario fan is subsequently reduced into a scenario tree that preserves representativeness of the stochastic process, providing a flexible and extensible framework for uncertainty modeling in stochastic programming problems. The framework can also be naturally applied to two-stage stochastic programming problems. Key contributions are the joint modeling of the stochastic process, preserving correlations between all random variables, and the automation of the complete workflow, including forecasting model selection, parameter estimation, scenario fan generation, and scenario tree construction. In addition, scentree does not assume any underlying data distribution, reducing the statistical expertise required to produce a scenario tree, and is modular by design, allowing external forecasts and other partial executions of the workflow.