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FUNDAMENTALS OF QUANTITATIVE FORECASTING
PART TWO SMOOTHING AND DECOMPOSITION
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accuracy actual adaptive filtering approach appropriate ARMA models ARRSES assessments autocorrelation coefficients Box-Jenkins calculated Chapter CM CM computed correlation cost cross autos cycle cyclical data of Table decision analysis described determine developed differencing different from zero econometric models economic error estimates example expected value F-test factors forecasting methods function future identify impulse response independent variables involved Kalman filters linear exponential smoothing MARMA mean squared error methodology methods of forecasting months moving average multicollinearity multiple regression obtained organization parameter values partial autocorrelations pattern planning predict problem procedure quantitative forecasting random ratio regression analysis regression equation relationship relevance trees residuals sample seasonally adjusted shown in Figure shown in Table significantly different single exponential smoothing situation smoothing methods specific standard stationary statistical techniques technological forecasting time-series time-series analysis trend trend-cycle users variance variation Wheelwright