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REGRESSION METHODS AND MOVING AVERAGES
EXPONENTIAL SMOOTHING METHODS
DISCOUNTED LEAST SQUARES AND DIRECT SMOOTHING
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analysis appropriate assume autocorrelation function autocovariance autoregressive process Bayesian Box-Jenkins Box-Jenkins models Chap coefficients computed constant model cumulative demand 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 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 period T+ 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