Sea Ice Extent Forecasting Project 🌎❄️

📑 Objective:

Forecast Arctic Sea Ice Extent over the next 20 years using time series analysis methods.

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📊 Dataset:

• Source: National Snow and Ice Data Center (NSIDC)
• Data: Monthly Sea Ice Extent (1979-2025)
• File: N_03_extent_v3.0.csv

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🔥 Models Built:

1. Polynomial Regression
2. ARIMA (1,1,1) Model
3. Prophet Model

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🧐 Exploratory Data Analysis:

• Identified a clear declining trend in sea ice extent over the years.
• Recent years show a sharper and faster decline.

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📈 Model Evaluations:

Model	RMSE	R²	Comments
Polynomial Regression	0.250	0.810	Great historical fit, unrealistic future
ARIMA (1,1,1)	2.402	-16.510	Poor fit, flat future
Prophet	0.251	0.808	Good fit, realistic trend with clipping

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🏆 Final Model Selected:

• Prophet Model (with clipping)
• Forecasts a realistic slow decline in sea ice extent.

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📂 Project Structure:

data/         # Dataset
notebooks/    # EDA + Modeling
outputs/      # Saved Plots
README.md     # Project report
requirements.txt # Libraries used

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🛠️ Tools and Libraries Used:

• Python
• Pandas
• Matplotlib / Seaborn
• Scikit-learn
• Statsmodels (ARIMA)
• Prophet (Facebook)
• Numpy

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📈 Final Forecast Plot:

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📢 Key Learnings:

• Importance of stationarity in time series modeling.
• Why polynomial models can overfit.
• How Prophet automatically handles trend and uncertainty.
• Importance of applying real-world constraints (e.g., clipping).

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📬 Contact

Vinit Singh Pathir
LinkedIn
Feel free to reach out if you want to collaborate on remote sensing, data science, or climate projects!

🌍 License

This project is open-source and free to use under the MIT License.

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🚀 Thank you for visiting the project!