Forecasting Principles and Applications
Published in January 1998
Book
Mixed media product
992 pages
978-0-256-23838-9 (ISBN)
Description
This text aims to offer a modern, comprehensive survey of the principles and applications of forecasting in the world of commerce. It includes the theory necessary for a statistics course, as well as examples, applications and cases drawn from a variety of sources. Modern theories such as state space, bootstrapping, Box-Jenkins, ARIMA intervention analysis and modern data analytic techniques are covered and a data disk with a forecasting program is included. The text gives comprehensive coverage of methods of forecasting, presenting real data sets, cases and problems. Boxed real applications are provided and the data disk contains a time series database with 300 actual time series included. Continuing minicases illustrate actual time series applications in an integrative forecasting process format, with a summary table providing an overall perspective of the forecasting process.
More details
Edition
New edition
Language
English
Place of publication
New York
United States
Publishing group
McGraw-Hill Education - Europe
Target group
College/higher education
Edition type
New edition
Illustrations
illustrations
Dimensions
Height: 230 mm
ISBN-13
978-0-256-23838-9 (9780256238389)
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Schweitzer Classification
Content
Part 1 Foundations of forecasting: planning and forecasting; statistical fundamentals for forecasting, appendices: 2-A expected values, 2-B Q-statistic for white noise ACK (k)s.; introduction to regression analysis, supplement 3 - cross correlation function. Part 2 Univariate methods: simple smoothing methods; decomposition and census II methods; trend-seasonal smoothing, supplement 6 - Fourier series analysis. Part 3 Univariate ARIMA methods: ARIMA introduction; ARIMA application; ARIMA forecast profiles. Part 4 Multivariate/causal methods: multiple regression of time series: econometric methods; multivariate ARIMA - transfer functions; multivariate ARIMA - intervention functions. Part 5 Cyclical qualitative, and artificial intelligence methods: cyclical forecasting methods; qualitative and technological forecasting; expert systems, neural networks, and genetic algorithms. Part 6 Combining validation, and managerial issues; combining control and validation methods; method characteristics, accuracy, and data sources.