Advances in Business and Management Forecasting Vol: 12

Kenneth D. Lawrence
New Jersey Institute of Technology, USA

Ronald K. Klimberg
Saint Joseph’s University, USA

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Product Details
23 Oct 2017
Emerald Publishing Limited
222 pages - 152 x 229mm
Advances in Business and Management Forecasting
Volume 12, Advances in Business and Management Forecasting, is a blind refereed serial publication. It presents state-of-the-art studies in the application of forecasting methodologies to such areas as supply chain, health care, prospecting for donations from university alumni, and the use of clustering and regression in forecasting. The orientation of this volume is for business applications for both the researcher and the practitioner of forecasting. 

Volume 12 is divided into three sections: Forecasting Applications, Predictive Analytics and Time Series. An interdisciplinary group of experts explore wide-ranging topics including multi-criteria scoring models, detecting rare events, the assessment of control charts for intermittent data, and fuzzy time series models.
1, The Effect of Released Information on Searching for Missing Children: The Case of the Baby Back Home Network; Yang, F. Nuermarti Y. Huang, Z. 
2, Enhanced Multicriteria Scoring Model; Ko, K. 
3, Forecasting Treatment Outcomes for the Futures Drug and Alcohol Rehabilitation Content; Miori, V., Campbell Garwood, K., Cardamone, C. 
4, An oversampling technique for classifying imbalanced datasets; Nguyen, S.,Quinn, J., Olinsky, A. 
5, Funding Analytics: A Predictive Analysis in a Major State Research University; Lawrence, K., Kudbya, S., Lawrence, S. M.
6, A Novel Approach to Forecasting Regression and Cluster Analysis; Klimberg, R., Ratick, S., Smith, H. 
7, Forecasting Development of a Practical and Effective Forecasting Performance Measure; Klimberg, R., Ratick, S. 
8, Assessing the Design of Control Charts for Intermittent Data; Lindsey, M., Pavur, R. 
9, On the Causal Models of Fuzzy Time Series; Duru, O. 
10, Modeling and Forecasting with Fuzzy Time Series and Artificial Neural Networks, Duru, O.; Butler, M. 
11, Forecasting in Service Supply Chain Systems: A State-of-the-Art Review Using Latent Semantic Analysis; Sudhanshu, J.
DR. KENNETH D. LAWRENCE is a Professor of Management Science and Business Analytics in the Tuchman School of Management at the New Jersey Institute of Technology. Professor Lawrence’s research is in the areas of applied management science, data mining, forecasting, and multi-criteria decision-making. His current research works include multi-criteria mathematical programming models for productivity analysis, discriminant analysis, portfolio modeling, quantitative finance, and forecasting/data mining. He is a full member of the Graduate Doctoral Faculty of Management at Rutgers, The State University of New Jersey in the Department of Management Science and Information Systems and a Research Fellow in the Center for Supply Chain Management in the Rutgers Business School. His research work has been cited over 1,750 times in various research publications.|DR. RONALD K. KLIMBERG is a Professor in the Department of Decision Systems Sciences of the Haub School of Business at Saint Joseph’s University. Dr. Klimberg has published 3 books, including his Fundamentals of Predictive Analytics Using JMP, edited 9 books, over 50 articles and made over 70 presentations at national and international conferences. His current major interest include multiple criteria decision making (MCDM), multiple objective linear programming (MOLP), data envelopment analysis (DEA), facility location, data visualization, data mining, risk analysis, workforce scheduling, and modeling in generation. He is currently a member of INFORMS, DSI, and MCDM. Ron was the 2007 recipients of the Tenglemann Award for his excellence in scholarship, teaching, and research.

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