In this paper, we present a comprehensive analysis of extreme temperature patterns using emerging statistical machine learning techniques. Our research focuses on exploring and comparing the effectiveness of various statistical models for climate time series forecasting. The models considered include Auto-Regressive Integrated Moving Average, Exponential Smoothing, Multilayer Perceptrons, and Gaussian Processes. We apply these methods to climate time series data from five most populated U.S. cities, utilizing Python and Julia to demonstrate the role of statistical computing in understanding climate change and its impacts. Our findings highlight the differences between the statistical methods and identify Multilayer Perceptrons as the most effective approach. Additionally, we project extreme temperatures using this best-performing method, up to 2030, and examine whether the temperature changes are greater than zero, thereby testing a hypothesis.

翻译：本文采用新兴统计机器学习技术，对极端温度模式进行了全面分析。研究聚焦于探索并比较多种统计模型在气候时间序列预测中的有效性，涉及的模型包括自回归积分滑动平均、指数平滑法、多层感知机以及高斯过程。我们将这些方法应用于美国人口最多的五个城市的气候时间序列数据，并利用Python和Julia展示统计计算在理解气候变化及其影响中的作用。研究结果揭示了不同统计方法之间的差异，并确定多层感知机为最有效的方法。此外，我们利用该最优方法对截至2030年的极端温度进行预测，检验温度变化是否显著大于零，从而验证了一项假设。