Understanding the business cycle is crucial for building economic stability, guiding business planning, and informing investment decisions. The business cycle refers to the recurring pattern of expansion and contraction in economic activity over time. Economic analysis is inherently complex, incorporating a myriad of factors (such as macroeconomic indicators, political decisions). This complexity makes it challenging to fully account for all variables when determining the current state of the economy and predicting its future trajectory in the upcoming months. The objective of this study is to investigate the capacity of machine learning models in automatically analyzing the state of the economic, with the goal of forecasting business phases (expansion, slowdown, recession and recovery) in the United States and the EuroZone. We compared three different machine learning approaches to classify the phases of the business cycle, and among them, the Multinomial Logistic Regression (MLR) achieved the best results. Specifically, MLR got the best results by achieving the accuracy of 65.25% (Top1) and 84.74% (Top2) for the EuroZone and 75% (Top1) and 92.14% (Top2) for the United States. These results demonstrate the potential of machine learning techniques to predict business cycles accurately, which can aid in making informed decisions in the fields of economics and finance.

翻译：理解商业周期对于构建经济稳定性、指导商业规划和为投资决策提供信息至关重要。商业周期指的是经济活动随时间推移呈现的扩张与收缩的循环模式。经济分析本质上是复杂的，涉及众多因素（如宏观经济指标、政治决策）。这种复杂性使得在确定经济当前状态并预测其未来数月轨迹时，难以全面考虑所有变量。本研究旨在探究机器学习模型自动分析经济状态的能力，以预测美国和欧元区的商业周期阶段（扩张、放缓、衰退和复苏）。我们比较了三种不同的机器学习方法来分类商业周期阶段，其中多项逻辑回归（MLR）取得了最佳结果。具体而言，MLR对欧元区的预测准确率达到了65.25%（Top1）和84.74%（Top2），对美国达到了75%（Top1）和92.14%（Top2）。这些结果表明机器学习技术在准确预测商业周期方面具有潜力，有助于在经济学和金融领域做出明智决策。