6)
Advances in business and management forecasting volume 6
Advances in Business and Management Forecasting
ISBN: 978-1-84855-548-8, eISBN: 978-1-84855-549-5
ISSN: 1477-4070
Publication date: 17 January 2009
Citation
(2009), "Advances in business and management forecasting volume 6", Lawrence, K.D. and Klimberg, R.K. (Ed.) Advances in Business and Management Forecasting (Advances in Business and Management Forecasting, Vol. 6), Emerald Group Publishing Limited, Leeds, p. iii. https://doi.org/10.1108/S1477-4070(2009)0000006020
Publisher
:Emerald Group Publishing Limited
Copyright © 2009, Emerald Group Publishing Limited
- Advances in business and management forecasting
- Advances in business and management forecasting volume 6
- Copyright page
- List of Contributors
- Editorial board
- Competitive set forecasting in the hotel industry with an application to hotel revenue management
- Predicting high-tech stock returns with financial performance measures: Evidence from Taiwan
- Forecasting informed trading at merger announcements: The use of liquidity trading
- Using data envelopment analysis (DEA) to forecast bank performance
- Forecasting demand using partially accumulated data
- Forecasting new adoptions: A comparative evaluation of three techniques of parameter estimation
- The use of a flexible diffusion model for forecasting national-level mobile telephone and internet diffusion
- Forecasting household response in database marketing: A latent trait approach
- A new basis for measuring and evaluating forecasting models
- Forecasting using internal markets, Delphi, and other approaches: The knowledge distribution grid
- The effect of correlation between demands on hierarchical forecasting
- Econometric count data forecasting and data mining (cluster analysis) applied to stochastic demand in truckload routing
- Two-attribute warranty policies under consumer preferences of usage and claims execution
- A dual transportation problem analysis for facility expansion/contraction decisions: A tutorial
- Make-to-order product demand forecasting: Exponential smoothing models with neural network correction