AbdelhakBen922/Time-Series-Analysis-and-Forecasting-of-Wind-Speed-in-Adrar-Algeria

# Wind Speed Time Series Analysis ## Overview This project focuses on Time Series Analysis (TSA) of wind speed data for locations in Algeria (Adrar) and Tunisia (Tataouine). By utilizing comparative meteorological datasets, this project aims to explore, analyze, and potentially forecast wind speed for power prediction applications. ## Project Structure - **`data/`**: Contains the meteorological datasets. - `data adrar.csv`: Wind speed data for Adrar, Algeria. - `data tataouine.csv`: Wind speed data for Tataouine, Tunisia. - `README.txt`: Documentation for the dataset. - **`notebooks/`**: Contains Jupyter notebooks for analysis. - `data exploration - python.ipynb`: Initial data exploration and visualization. - `wind_speed_adrar_tsa.ipynb`: Time series modeling and analysis specifically for the Adrar dataset. - **`pyproject.toml`**: Contains project metadata and dependencies. ## Dependencies The project uses Python 3.13+ and relies on standard data science and time-series libraries: - `pandas` - Data manipulation and analysis - `matplotlib` & `seaborn` - Data visualization - `statsmodels` - Statistical modeling and time series analysis - `ipykernel` - Jupyter Notebook support ## Setup Instructions 1. **Set up the virtual environment:** ```bash python -m venv .venv source .venv/bin/activate # On Linux/macOS ``` 2. **Install dependencies:** Since the project uses `pyproject.toml`, you can install the dependencies via pip: ```bash pip install -e . ``` 3. **Run the notebooks:** Start Jupyter Lab or use VS Code's native notebook interface to open the files in the `notebooks/` directory.