## Forecasting Principles and Applications |

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### Contents

Statistical Fundamentals for Forecasting | 36 |

Simple Linear Regression Analysis | 92 |

Simple Smoothing Methods | 147 |

Copyright | |

27 other sections not shown

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### Common terms and phrases

accuracy ACFs and PACFs actual values advertising alpha applications ARIMA model autocorrelation Big City Bookstore Box-Jenkins calculated causal Chapter constant cross-correlation cycles cyclical decomposition demand denotes developed differences discussed Durbin-Watson statistic economic effect Equation estimate example expected value exponential smoothing F-test forecast errors forecasting methods forecasting model forecasting systems Gompertz curve graphs heteroscedasticity identify illustrates increase independent variables influences input intervention Journal of Forecasting least squares linear logarithms mean measures Minicase monthly months moving average multicollinearity multivariate node nonlinear nonstationary outliers output patterns percent period prediction intervals problem random walk regression analysis regression coefficients relationship sample seasonal indexes simple squared errors standard deviation standard error stationarity statistically significant Std Error stock index Sum of squared tracking signal transfer function trend univariate Usable observations valid variance weights white noise yields zero