Time Series Forecasting in Python - Tutorial for Beginners

Learn the fundamental concepts of time series forecasting through this comprehensive 93-minute tutorial designed specifically for beginners with no prior experience in forecasting. Start by understanding what constitutes a time series and explore baseline forecasting models as your foundation. Master the ARIMA (AutoRegressive Integrated Moving Average) model, one of the most widely used statistical methods for time series prediction, with hands-on coding implementation in Python. Discover proper cross-validation techniques specifically tailored for time series data to ensure robust model evaluation. Advance to forecasting with exogenous features, learning how to incorporate external variables that can improve prediction accuracy. Understand prediction intervals to quantify uncertainty in your forecasts and make more informed decisions. Explore essential evaluation metrics including MAE, RMSE, and MAPE to assess model performance effectively. Each theoretical concept is immediately followed by practical Python coding sessions, allowing you to implement baseline models, ARIMA, cross-validation, exogenous features, and prediction intervals using real datasets. Access the complete solutions notebook and dataset through provided GitHub links to practice alongside the tutorial and reinforce your learning through hands-on experience.