Learn practical time series forecasting with Python using real-world datasets from energy (EIA – U.S. Energy Information Administration) and economics (FRED – Federal Reserve Economic Data).
Build skills step by step, from loading and preprocessing time series data to decomposing trends and seasonality, visualizing patterns with Plotly, and applying forecasting models like ARIMA, SARIMA, exponential smoothing, and Prophet. Learn to evaluate model performance using error metrics and cross-validation techniques like walk-forward validation.
The course emphasizes hands-on exercises in a GitHub Codespaces environment, so you can immediately apply what you learn to your own datasets. Whether you’re working with sales, energy, or financial data, you’ll gain the skills to generate accurate, interpretable forecasts that drive real-world decisions.
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