Advances in Business and Management Forecasting
Published on 9. November 2017
Book
Hardback
224 pages
978-1-78743-070-9 (ISBN)
Description
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.
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.
Reviews / Votes
Business scholars discuss forecasting applications; predictive analytics, regression analysis, and clustering in forecasting; and time series, intermittent data, and supply chain applications. Among the topics are the effect of releasing information on searching for missing children: the case of the Baby Back Home network, forecasting treatment outcomes for the Futures drug and alcohol rehabilitation center, a novel approach to forecasting regression and cluster analysis, developing a practical and effective forecasting performance measure, and forecasting in-service supply chain systems: a state-of-the-art review using latent semantic analysis. -- Annotation (c)2017 * (protoview.com) *More details
Series
Language
English
Place of publication
Bingley
United Kingdom
Target group
Professional and scholarly
Dimensions
Height: 235 mm
Width: 157 mm
Thickness: 17 mm
Weight
482 gr
ISBN-13
978-1-78743-070-9 (9781787430709)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Kenneth D. Lawrence | Ronald K. Klimberg
Advances in Business and Management Forecasting
E-Book
11/2017
1st Edition
Emerald Publishing Limited
€104.99
Available for download
Persons
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.
Content
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.
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.