Part 1

Working with time series data

1Time series objects: ts, xts, tsibble→2Tidy time series with tsibble→3Visualizing time series→4Time series EDA→5Calendar effects: trading days, holidays, time zones→6Missing values and gaps in time series→

Part 2

Patterns: trend, seasonality, cycles

7Decomposition: STL vs classical→8MSTL: multiple seasonal decomposition→9Stationarity and unit roots→10ACF and PACF→11Time series features with feasts→12Spectral analysis→

Part 3

The forecaster's toolbox

13Benchmark forecasts: naive, seasonal naive, drift→14Box-Cox and transformations→15Moving averages and smoothing→16Forecast accuracy: MAE to MASE→17Time series cross-validation→18Prediction intervals→19Bootstrapping and bagging forecasts→20Choosing a forecasting model→

Part 4

Exponential smoothing

21Simple exponential smoothing→22Holt's linear trend→23Holt-Winters seasonality→24The ETS framework→25ETS vs ARIMA→

Part 5

The ARIMA family

26ARIMA models→27Choosing p, d, q→28auto.arima→29Residual diagnostics→30Seasonal ARIMA→31SARIMA the tidy way→32Long memory: ARFIMA→

Part 6

Regression with time series

33Time series regression: TSLM and spurious regression→34ARIMAX: external regressors→35Dynamic regression→36Fourier terms for seasonality→37Intervention analysis and CausalImpact→

Part 7

Complex seasonality and modern frameworks

38TBATS: multiple seasonalities→39High-frequency data→40The sub-daily forecasting playbook→41Prophet→42The fable workflow→

Part 8

Multivariate and structural models

43VAR models→44Granger causality and impulse responses→45Cointegration and error correction→46State space and the Kalman filter→47Structural time series with bsts→48GARCH volatility→49Regime switching: TAR and STAR→

Part 9

Machine learning for forecasting

50Feature engineering for forecasting→51Gradient boosting for time series→52Global models: learning across many series→53Neural network forecasting→54Deep learning for forecasting: an honest guide→55Conformal prediction intervals→

Part 10

Probabilistic forecasting and evaluation

56Distributional forecasts in fable→57Scoring rules: CRPS and pinball loss→58Combining forecasts→59Backtesting design at scale→60What the M competitions teach→

Part 11

Difficult series

61Anomaly detection→62Changepoint detection→63Intermittent demand: Croston and friends→64Count time series→65Very short and very long series→66Forecasting through structural breaks→

Part 12

Hierarchical and grouped forecasting

67Hierarchical and grouped time series→68Forecast reconciliation: bottom-up to middle-out→69Optimal reconciliation: MinT with fable→

Part 13

Forecasting in production

70Forecast hundreds of series automatically→71Monitoring forecasts in production→72Forecast value added: judgment and overrides→73Case study: retail demand end to end→74Case study: energy load end to end→