global-temp-analysis-prediction
A project analyzing and forecasting global land-ocean temperature trends using time series (ARIMA) and nonlinear regression models. This study highlights historical patterns, accelerating warming trends, and provides predictions to support climate change mitigation efforts.
https://github.com/sungjuneom/global-temp-analysis-prediction
Science Score: 44.0%
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Low similarity (11.7%) to scientific vocabulary
Repository
A project analyzing and forecasting global land-ocean temperature trends using time series (ARIMA) and nonlinear regression models. This study highlights historical patterns, accelerating warming trends, and provides predictions to support climate change mitigation efforts.
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Metadata Files
README.md
Global Land-Ocean Temperature Index Analysis and Prediction
This repository contains the code and methodology used in the study titled "Analysis and Prediction Modeling of Global Land-Ocean Temperature Indices Using Time Series and Nonlinear Regression". This research aims to systematically analyze and predict the trends of global land-ocean temperature indices as indicators of climate change.
Click here for the full paper (Korean)
Overview
Research Objectives
- Analysis: Evaluate historical trends in global land-ocean temperature indices using time series and nonlinear regression methods.
- Prediction: Develop predictive models to estimate future trends in temperature indices.
- Insights: Provide actionable insights into the accelerating pace of global warming and its implications.
Key Features
- Data Source: NASA Goddard Institute for Space Studies (GISS) Global Land-Ocean Temperature Index (1880–2023).
- Methods:
- Time Series Analysis: ARIMA modeling.
- Nonlinear Regression: Quadratic modeling to capture nonlinear trends.
- Results:
- Predictive trends of global temperature indices for the next decade.
- Comprehensive analysis of historical data, including trends and seasonality.
Results
- ARIMA Model:
- Captured short-term fluctuations and long-term trends.
- Predicts a gradual rise in global temperature indices over the next decade.
- Nonlinear Regression:
- Highlights accelerated warming trends, with quadratic components capturing exponential growth in temperature increases.
Key Findings
- Global temperature indices exhibit consistent upward trends, with significant acceleration post-1970s.
- Nonlinear regression reveals that temperature increases are better modeled using quadratic trends, indicating rapid climate change.
## Future Work - Incorporate external variables (e.g., greenhouse gas concentrations, solar activity) for enhanced model robustness.
- Expand the analysis to regional datasets for localized climate insights.
- Explore machine learning approaches for long-term climate modeling.
License
This project is licensed under the MIT License.
Acknowledgments
- Data provided by NASA GISS.
BibTeX
``` @software{Eom2025Software, author = {Sungjun Eom}, title = {Global Land-Ocean Temperature Index Analysis and Prediction}, year = {2025}, url = {https://github.com/SungjunEom/global-temp-analysis-prediction}, orcid = {0009-0001-8013-2454}, license = {MIT}, note = {If you use this repository, please cite the software and/or the associated article.} }
@article{Eom2025Article, author = {Sungjun Eom}, title = {Global Land-Ocean Temperature Index Analysis and Prediction}, year = {2025}, url = {https://github.com/SungjunEom/global-temp-analysis-prediction}, note = {Paper version.} }
@misc{Eom2025Github, author = {Sungjun Eom}, title = {Global Land-Ocean Temperature Index Analysis and Prediction}, url = {https://github.com/SungjunEom/global-temp-analysis-prediction}, note = {Accessed: 2025-01-14}, year = {2025} } ```
Owner
- Name: Sungjun Eom
- Login: SungjunEom
- Kind: user
- Location: Seoul
- Repositories: 2
- Profile: https://github.com/SungjunEom
Electrical and Computer Engineering,University of Seoul(2018~)
Citation (CITATION.cff)
cff-version: 1.2.0
message: "If you use this repository, please cite the software and/or the associated article as below."
type: software
authors:
- family-names: "Eom"
given-names: "Sungjun"
orcid: "https://orcid.org/0009-0001-8013-2454"
title: "Global Land-Ocean Temperature Index Analysis and Prediction"
# version: 2.0.4
# doi: 10.5281/zenodo.1234
date-released: 2025-01-12
url: "https://github.com/SungjunEom/global-temp-analysis-prediction"
license: "MIT"
related_resources:
type: article
title: "Global Land-Ocean Temperature Index Analysis and Prediction"
authors:
- family-names: "Eom"
given-names: "Sungjun"
orcid: "https://orcid.org/0009-0001-8013-2454"
# journal: "Journal of Example Research"
# volume: "12"
# issue: "3"
# pages: "45-67"
year: 2025
# doi: "10.1234/example.doi"
url: "https://github.com/SungjunEom/global-temp-analysis-prediction"
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