Forecasting principles and applications

Includes index
Accompanied by a disk containing data files and spreadsheets.

Foundation forecasting; univariate methods; univariate ARIMA methods; multivariate/causal methods; cyclical, qualitative and artificial intelligence methods; combining, validation and managerial issues.

This text provides a modern comprehensive survey of the principles and applications of forecasting methods in the world of commerce, public and private. The text includes all necessary theory for a rigorous statistics approach, but all basic content is presented in an intuitive style supported with applications drawn from a wide variety of real sources. The text is appropriate for advanced undergraduate or graduate courses in Business, Economics, or Engineering, as well as time series or regression/forecasting in statistics departments where an applications-oriented approach is desired. Coverage ranges from a review of basic statistics, to thorough discussions of basic methods such as smoothing, trend, and regression, up to advanced techniques such as ARIMA, MARIMA, Neural Networks, Econometrics, and Intervention Analysis. Modern Data Analytic techniques are included. Over 300 time series data sets are included on disk and continuing minicases help student understanding, short interest boxes are included for motivation, and each chapter includes a thorough collection of problems for assignment.