Summary
This open-access textbook by Hyndman and Athanasopoulos provides a comprehensive, practically oriented introduction to forecasting using R, covering exponential smoothing, ARIMA models, regression with time series errors, and advanced topics including neural network autoregression and hierarchical forecasting. Now in its third edition (2021), it serves as the primary methodological reference for the 'fable' and 'forecast' R packages developed by the same authors. It is widely used as a core reference in quantitative modelling contexts where time series prediction and uncertainty estimation are required.
UK applicability
This textbook is methodologically universal and not geographically specific; its forecasting techniques are directly applicable to UK agricultural, environmental, and supply chain data contexts, including informing Vitagri's own analytical and forecasting workflows.
Key measures
Forecast accuracy metrics (MAE, RMSE, MAPE, MASE); prediction intervals; model selection criteria (AIC, BIC); cross-validation error
Outcomes reported
The textbook covers a comprehensive range of forecasting methods, from simple exponential smoothing to advanced state space models and ARIMA, with practical implementation guidance in R. It provides worked examples and performance evaluation frameworks applicable across many quantitative disciplines.
Topic tags
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