- Ch 1: Understanding time series forecasting
- Ch 2: A naïve prediction of the future
- Ch 3: Going on a random walk
- Ch 4: Modeling a moving average process
- Ch 5: Modeling an autoregressive process
- Ch 6: Modeling complex time series
- Ch 7: Forecasting non-stationary time series
- Ch 8: Accounting for seasonality
- Ch 9: Adding external variables to our model
- Ch 10: Forecasting multiple time series
- Ch 11: Captonse project - Forecasting the number of anti-diabetic drug prescriptions in Australia
- Ch 12: Introducing deep learning for time series forecasting
- Ch 13: Data windowing and creating baselines for deep learning
- Ch 14: Baby steps with deep learning
- Ch 15: Remembering the past with LSTM
- Ch 16: Filtering our time series with CNN
- Ch 17: Using predictions to make more predictions
- Ch 18: Capstone project - Forecasting the electric power consumption of a household
- Ch 19: Automating time series forecasting with Prophet
- Ch 20: Capstone project - Forecasting the monthly average retail price of steak in Canada
