https://github.com/shub-garg/global-temperature-prediction-and-analysis-using-arima-sarimax-and-neural-network
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
https://github.com/shub-garg/global-temperature-prediction-and-analysis-using-arima-sarimax-and-neural-network
arima-forecasting climate-change forecasting-models global-temperature nasa-data neural-network sarimax time-series-analysis
Last synced: 3 months ago
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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
- Host: GitHub
- URL: https://github.com/shub-garg/global-temperature-prediction-and-analysis-using-arima-sarimax-and-neural-network
- Owner: shub-garg
- License: mit
- Created: 2023-12-12T04:55:01.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-05-31T19:43:39.000Z (over 1 year ago)
- Last Synced: 2025-04-30T10:24:12.794Z (5 months ago)
- Topics: arima-forecasting, climate-change, forecasting-models, global-temperature, nasa-data, neural-network, sarimax, time-series-analysis
- Language: Jupyter Notebook
- Homepage:
- Size: 6.6 MB
- Stars: 7
- Watchers: 3
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Global Temperature Prediction and Analysis
## Overview
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 NASA.
## Table of Contents
- [Project Structure](#project-structure)
- [Data](#data)
- [Analysis and Modeling](#analysis-and-modeling)
- [Results](#results)
- [How to Run](#how-to-run)
- [Dependencies](#dependencies)
- [Contributing](#contributing)
- [License](#license)
## Project Structure
- **global_temp_nn/**: Jupyter notebooks for data exploration, cleaning, analysis, and neural network implementation.
- **arima_sarima/**: Jupyter notebooks for ARIMA and SARIMAX time series forecasting.
- **images/**: Contains images generated during the analysis.
- **data/**: Dataset files used in the project.
- **README.md**: Project overview and instructions.
- **temperature_video_june.avi**: Temperature anomalies plotted on a world map from 1880 to 2023.
## Video
## Data
The dataset used in this project includes:
- Global Surface Temperatures
- Northern Hemisphere Temperatures
- Southern Hemisphere Temperatures
- Zonal Temperatures
- Extended Reconstruction SSTs Version 5 (ERSSTv5) (NetCDF file)
## Analysis and Modeling
- **Exploratory Data Analysis (EDA)**: Initial exploration of the dataset to understand patterns and trends.
- **Data Cleaning**: Handling missing values, interpolation, and ensuring data integrity.
- **Time Series Forecasting**: Utilizing ARIMA and SARIMAX for time series forecasting.
- **Neural Network Models**: Implementing neural networks for more complex analyses.
## Results
- Visualizations of temperature anomalies over time.
- Forecasts of future temperature trends using ARIMA and SARIMAX.
- Neural network predictions for complex analysis of temperature patterns.
## How to Run
Download the zip file and run the individual Jupyter notebooks:
1. `global_temp_nn.ipynb`
2. `arima_sarimax.ipynb`
## Dependencies
- TensorFlow
- Statsmodels
Install the required packages using:
```bash
pip install tensorflow statsmodels
```
## Contributing
Shubham Garg- sg8311@nyu.edu
Raunak Shukla - rs8668@nyu.edu
Phani Varma Gadiraju - pg2542@nyu.edu
## License
This project is licensed under the [MIT License](LICENSE).