There are a couple of sections that also require knowledge of matrices, but these are flagged.Īt the end of each chapter we provide a list of “further reading”. We use it ourselves for masters students and third-year undergraduate students at Monash University, Australia.įor most sections, we only assume that readers are familiar with introductory statistics, and with high-school algebra. The book is written for three audiences: (1) people finding themselves doing forecasting in business when they may not have had any formal training in the area (2) undergraduate students studying business (3) MBA students doing a forecasting elective. We don’t attempt to give a thorough discussion of the theoretical details behind each method, although the references at the end of each chapter will fill in many of those details. This textbook is intended to provide a comprehensive introduction to forecasting methods and to present enough information about each method for readers to be able to use them sensibly. Welcome to our online textbook on forecasting. 13.9 Dealing with outliers and missing values.13.8 Forecasting on training and test sets.13.7 Very long and very short time series.13.5 Prediction intervals for aggregates.13.3 Ensuring forecasts stay within limits.11.6 Forecasting Australian prison population.11.5 Reconciled distributional forecasts.11.4 Forecasting Australian domestic tourism.11.1 Hierarchical and grouped time series.

11 Forecasting hierarchical and grouped time series.10.4 Stochastic and deterministic trends.10.2 Regression with ARIMA errors using fable.8.5 Innovations state space models for exponential smoothing.8.4 A taxonomy of exponential smoothing methods.7.8 Correlation, causation and forecasting.5.9 Evaluating distributional forecast accuracy.5.5 Distributional forecasts and prediction intervals.3.5 Methods used by official statistics agencies.1.7 The statistical forecasting perspective.

1.6 The basic steps in a forecasting task.

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