Forecasting: Methods and Applications
Since accurate forecasting requires more than just inserting historical data into a model, Forecasting: Methods and Applications, 3/e, adopts a managerial, business orientation. Integrated throughout this text is the innovative idea that explaining the past is not adequate for predicting the future. Inside, you will find the latest techniques used by managers in business today, discover the importance of forecasting and learn how it's accomplished. And you'll develop the necessary skills to meet the increased demand for thoughtful and realistic forecasts. New features in the third edition include an emphasis placed on the practical uses of forecasting; all data sets used in this book are available on the Internet; comprehensive coverage provided on both quantitative and qualitative forecasting techniques; and includes many new developments in forecasting methodology and practice.
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analysis appropriate ARIMA models ARMA ARRSES autoregressive Box-Jenkins calculated changes Chapter coefficient confidence intervals correlation cycle D(EOM data set denotes deviation differencing dummy variables dynamic regression model econometric equation estimated example explanatory variables exponential smoothing exponential smoothing methods extrapolation F-test Figure forecast variable forecasting accuracy forecasting errors forecasting methods forecasting model function future Holt-Winters Holt's method Journal of Forecasting judgmental least squares linear regression long-term Makridakis MAPE mean squared error measure mileage month monthly multicollinearity multiple regression non-linear non-stationary obtained P-value packages parameters partial autocorrelation pattern period plot prediction intervals procedure production regression model relationship residuals sample scatterplot seasonal component seasonally adjusted Section selected series models shown shows significant simple regression single exponential smoothing squared errors standard error stationary statistical Step Table trend trend-cycle users values variance variation white noise zero