By Robert A. Yaffee
Supplying a transparent rationalization of the elemental idea of time sequence research and forecasting, this publication concept with purposes of 2 renowned statistical packages--SAS and SPSS. The textual content examines relocating general, exponential smoothing, Census X-11 deseasonalization, ARIMA, intervention, move functionality, and autoregressive blunders types and has short discussions of ARCH and GARCH versions. The e-book beneficial properties remedies of forecast development with regression and autoregression mixture versions and version and forecast overview, in addition to a pattern measurement research for universal time sequence versions to achieve sufficient statistical strength. to reinforce the book's worth as a instructing software, the information units and courses utilized in the publication are made to be had at the educational Press site. The cautious linkage of the theoretical constructs with the sensible issues occupied with using the statistical programs makes it effortless for the person to correctly follow those ideas. Key beneficial properties* Describes central ways to time sequence research and forecasting* provides examples from public opinion study, coverage research, political technological know-how, economics, and sociology* unfastened website comprises the information utilized in such a lot chapters, facilitating studying* Math point pitched to normal social technological know-how utilization* thesaurus makes the cloth available for readers in any respect degrees
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Additional info for An Introduction to Time Series Analysis and Forecasting: With Applications of SASA® and SPSSA®
Because of the forecast into the future horizon, the output speciﬁes how many cases have been added to the dataset. 2 on the basis of an objective criterion of a minimum sum of squared errors. sps. 31557 Not used DFE = 261. 20 24 new cases have been added. The goodness of ﬁt of the model is tested with the next command, FIT err_1 /DFE=261. 0 Algorithms, 1997). They are given in the output of the smoothing and can be entered in this command thereafter. The output contains a few measures of ﬁt, such as mean error, mean absolute error, mean square error, and root mean square error.
If the series is an extension of a previous series, then the average of that previous series may be used. Alternatively, the mean of the whole series may be used as a starting value. Different starting values may be employed while some measure of forecasting accuracy may be compared to see which will be ultimately chosen as the best. Finally, backcasting, using an ARIMA model to be discussed in Chapters Four through Seven, may be employed. Based on the existing data in the series, the analyst may forecast backward to obtain the initial value.
2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 3. CENTERED MOVING AVERAGES Calculation of the moving average differs depending on whether the series contains an even or odd number of observations. Many series have even numbers of observations, such as those spanning a year of 12 months. , 1983). Rarely, however, is the naive forecast using a single moving average tendered except as an impromptu approximation for general purposes. In cases such as this, centering the moving average solves the problem. Centering involves taking an additional moving average of order 2 of the ﬁrst moving averages.
An Introduction to Time Series Analysis and Forecasting: With Applications of SASA® and SPSSA® by Robert A. Yaffee