Forecasting: principles and practiceForecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.
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Contents
| 7 | |
| 11 | |
| 27 | |
The forecasters toolbox | 47 |
Judgmental forecasts | 83 |
Time series regression models | 103 |
Time series decomposition | 157 |
Exponential smoothing | 183 |
Dynamic regression models | 275 |
Forecasting hierarchical or grouped time series | 297 |
Advanced forecasting methods | 321 |
Some practical forecasting issues | 347 |
Using R | 369 |
| 371 | |
| 377 | |
ARIMA models | 221 |
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AICc antidiabetic drug approach ARIMA model Australia auto.arima autocorrelation autolayer autoplot base forecasts beer production bottom-level Box-Cox transformation Chapter coefficients coherent forecasts compute correlation daily data set decomposition Delphi method dummy variables electrical equipment electricity demand equation estimated ETS model example exponential smoothing fitted values forecast errors forecasting methods Fourier terms function Holt-Winters Hyndman judgmental forecasting lags level 80 linear linear regression Ljung-Box test matrix monthly moving average multiplicative seasonality naļve forecasts naļve method non-seasonal non-stationary normally distributed observations obtained outliers p-value PACF PI=FALSE plot point forecasts prediction intervals predictor variables produce forecasts random regression model residuals RMSE scatterplots seasonal component seasonal pattern seasonal period seasonally adjusted seasonally adjusted data seasonally differenced Section shown in Figure shows simple exponential smoothing stationary temperature test set training data training set trend trend-cycle variance variation visitor nights white noise xlab("Year y₁ ylab zero