## Forecasting and time series analysis |

### What people are saying - Write a review

User Review - Flag as inappropriate

forecast

### Contents

INTRODUCTION TO FORECASTING SYSTEMS | 1 |

REGRESSION METHODS AND MOVING AVERAGES | 19 |

EXPONENTIAL SMOOTHING METHODS | 48 |

Copyright | |

23 other sections not shown

### Other editions - View all

### Common terms and phrases

analysis appropriate assume autocorrelation function autocovariance autoregressive process Bayesian Box-Jenkins Box-Jenkins models calculations Chap coefficients computed constant model cumulative demand current-origin defined demand in period demand process develop direct smoothing double smoothing end of period example expected demand expected value forecast error forecast for period forecasting equation forecasting model forecasting procedure forecasting system historical data independent variables initial estimates least-squares estimates linear regression linear trend model mean absolute deviation model parameters multiple nonstationary normal equations normally distributed observations obtain origin partial autocorrelation partial autocorrelation function permanent component polynomial posterior distribution prediction interval probability distribution random variable regression model residual sample autocorrelation function seasonal factor seasonal model series model shown in Fig simple exponential smoothing simple moving average simulation smoothed error smoothed statistics smoothing constant smoothing vector starting values stationary Suppose transition matrix trend component unbiased unknown parameters variance-covariance matrix weights xT+T xT+T(T zero