Emerging Statistical Machine Learning Techniques for Extreme Temperature Forecasting in U.S. Cities

TL;DR

This paper compares various statistical machine learning models for extreme temperature forecasting in U.S. cities, identifying Multilayer Perceptrons as the most effective and projecting future temperature trends up to 2030.

Contribution

It provides a comprehensive comparison of statistical models for climate forecasting and demonstrates the application of machine learning techniques to extreme temperature prediction.

Findings

Multilayer Perceptrons outperform other models in accuracy.

Projected temperature increases suggest significant climate change impacts.

Statistical models help understand and predict extreme temperature patterns.

Abstract

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.