📰 Data (and some R code) behind NY Times feature https://www.nytimes.com/interactive/2018/01/21/world/year-in-weather.html#pwm w/some R code

This project aims to investigate temperature changes over time and predict future temperature patterns on a regional and global scale. We employ time series forecasting methods, including neural networks, ARIMA, and SARIMAX, using the GISTEMP v4 dataset from NA

Global surface temperature layers are interpolated based on a point measurement data set of the worldwide surface temperature, which has been recorded since 1950. For the spatial interpolation, an universal Kriging approach is applied with additional layers for the continentality, the atmospheric distance, the North-South topographic gradient an…

Exploring deaths by natural disasters, and possible relations to climate change.

Time series analysis is performed on the Berkeley Earth Surface Temperature dataset.

Visualizes 'Co2 Emissions' and 'Global Temperature' as a graph.

MAPS fellowship presentation - May 2018 - Global Warming Hiatus Statistics

A Python library for querying global temperature data by latitude and longitude

[Paper] Basic signal processing on global temperature to form my own opinion concerning the climate warming

A set of exploratory and forecasting methods conducted on multiple datasets concerning global warming variables, utilizing Python

Wolfram Mathematica programming project to practice manipulation and visualization of datasets.

Analyze and forecast global CO₂ trends using real-world climate data. This project applies data processing, visualization, and machine learning (linear regression) to explore patterns in air quality and predict future carbon dioxide levels.

Climate Change Analysis

Demonstração de Criação e Análise de Suavização de Médias Móveis Simples e Centralizada em séries temporais.

This project will show the time series analysis of Global Temperature Dataset which is from 1900 - 2013. It has been build in R programming Language..

This project demonstrates the application of real-world data wrangling techniques using Python. The goal was to analyze the relationship between global temperature anomalies and CO₂ emissions over time.

Shiny app calculating linear trends in global temperatures