Forecasting, to estimate future events, is crucial for business and decision-making. This paper proposes QxEAI, a methodology that produces a probabilistic forecast that utilizes a quantum-like evolutionary algorithm based on training a quantum-like logic decision tree and a classical value tree on a small number of related time series. We demonstrate how the application of our quantum-like evolutionary algorithm to forecasting can overcome the challenges faced by classical and other machine learning approaches. By using three real-world datasets (Dow Jones Index, retail sales, gas consumption), we show how our methodology produces accurate forecasts while requiring little to none manual work.

翻译：预测旨在估计未来事件，对商业活动和决策制定至关重要。本文提出QxEAI方法，该方法通过基于少量相关时间序列训练类量子逻辑决策树与经典值树，利用类量子进化算法生成概率预测。我们展示了将类量子进化算法应用于预测领域，如何能够克服经典方法及其他机器学习方法所面临的挑战。通过使用三个真实世界数据集（道琼斯指数、零售销售额、天然气消耗量），我们证明了该方法能够在几乎无需人工干预的情况下生成精确的预测结果。