Emerging Statistical Machine Learning Techniques for Extreme Temperature
Forecasting in U.S. Cities
- URL: http://arxiv.org/abs/2307.14285v1
- Date: Wed, 26 Jul 2023 16:38:32 GMT
- Title: Emerging Statistical Machine Learning Techniques for Extreme Temperature
Forecasting in U.S. Cities
- Authors: Kameron B. Kinast and Ernest Fokou\'e
- Abstract summary: We present a comprehensive analysis of extreme temperature patterns using emerging statistical machine learning techniques.
We apply these methods to climate time series data from five most populated U.S. cities.
Our findings highlight the differences between the statistical methods and identify Multilayer Perceptrons as the most effective approach.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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