IR Applications. Volume 15, January 18, 2008. ©Copyright 2008. And linear regression model are. Ascertain demands for programs. Time series analysis, fuzzy time series. Business Forecasting (4th Edition). Will be available for download by AIR members as a PDF document.

Author: Robert A. Yaffee ISBN: 702 Genre: Mathematics File Size: 66.58 MB Format: PDF, ePub, Docs Download: 235 Read: 780 Providing a clear explanation of the fundamental theory of time series analysis and forecasting, this book couples theory with applications of two popular statistical packages--SAS and SPSS. The text examines moving average, exponential smoothing, Census X-11 deseasonalization, ARIMA, intervention, transfer function, and autoregressive error models and has brief discussions of ARCH and GARCH models. The book features treatments of forecast improvement with regression and autoregression combination models and model and forecast evaluation, along with a sample size analysis for common time series models to attain adequate statistical power.

To enhance the book's value as a teaching tool, the data sets and programs used in the book are made available on the Academic Press Web site. The careful linkage of the theoretical constructs with the practical considerations involved in utilizing the statistical packages makes it easy for the user to properly apply these techniques. Key Features * Describes principal approaches to time series analysis and forecasting * Presents examples from public opinion research, policy analysis, political science, economics, and sociology * Free Web site contains the data used in most chapters, facilitating learning * Math level pitched to general social science usage * Glossary makes the material accessible for readers at all levels Category: Mathematics. Author: Douglas C. Montgomery ISBN: 502 Genre: Mathematics File Size: 84.61 MB Format: PDF, ePub, Mobi Download: 663 Read: 911 An accessible introduction to the most current thinking in and practicality of forecasting techniques in the context of time-oriented data.

Analyzing time-oriented data and forecasting are among the most important problems that analysts face across many fields, ranging from finance and economics to production operations and the natural sciences. As a result, there is a widespread need for large groups of people in a variety of fields to understand the basic concepts of time series analysis and forecasting. Blank kvitanc na oplatu gazu 1. Introduction to Time Series Analysis and Forecasting presents the time series analysis branch of applied statistics as the underlying methodology for developing practical forecasts, and it also bridges the gap between theory and practice by equipping readers with the tools needed to analyze time-oriented data and construct useful, short- to medium-term, statistically based forecasts. Author: Andy Pole ISBN: 433 Genre: Mathematics File Size: 62.38 MB Format: PDF, Kindle Download: 341 Read: 1247 Practical in its approach, Applied Bayesian Forecasting and Time Series Analysis provides the theories, methods, and tools necessary for forecasting and the analysis of time series. The authors unify the concepts, model forms, and modeling requirements within the framework of the dynamic linear mode (DLM).

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