Time Series Forecasting in Python – Tutorial for Beginners

Introduction to Time Series00:00:00

An overview of time series data, defining its key components: trend, seasonality, and residuals, while explaining the importance of statistical models as a foundation for advanced forecasting.

Developing Baseline Models00:06:33

Introduces essential baseline models like naive, historic average, window average, and seasonal naive. Explains how these provide necessary benchmarks for evaluating complex models.

Forecasting with ARIMA and SARIMA00:24:15

Deep dive into the ARIMA (AutoRegressive Integrated Moving Average) model and its seasonal variant, SARIMA. Explains how auto ARIMA automates the selection of optimal parameters.

Cross-Validation for Robust Evaluation00:39:10

Covers the critical concept of time series cross-validation, emphasizing why it is superior to a simple train-test split for evaluating model performance over multiple windows.

Incorporating Exogenous Features00:50:48

Discusses how to include external variables (like pricing or time-based features) to improve forecasts, noting how to handle future values of these features.

Prediction Intervals and Model Evaluation01:09:03

Explains probabilistic forecasting using prediction intervals and discusses various evaluation metrics, including MAE, SMAPE, MASE, and CRPS, to determine model success.