최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기Technological forecasting and social change, v.86, 2014년, pp.49 - 64
Lee, H. , Kim, S.G. , Park, H.w. , Kang, P.
This study proposes a novel approach to the pre-launch forecasting of new product demand based on the Bass model and statistical and machine learning algorithms. The Bass model is used to explain the diffusion process of products while statistical and machine learning algorithms are employed to pred...
Interfaces Bass 31 S82 2001 10.1287/inte.31.3s.82.9677 DIRECTV: forecasting diffusion of a new technology prior to product launch
Ozkaya 2008 Demand management in global supply chains
Rogers 1995 Diffusion of Innovations
Int. J. Forecast. Meade 22 519 2006 10.1016/j.ijforecast.2006.01.005 Modelling and forecasting the diffusion of innovation-a 25-year review
Manag. Sci. Bass 15 215 1969 10.1287/mnsc.15.5.215 A new product growth model for consumer durables
Technol. Forecast. Soc. Change Turk 79 85 2012 10.1016/j.techfore.2011.06.010 Bass model estimates for broadband diffusion in European countries
Manag. Sci. Lilien 27 493 1981 10.1287/mnsc.27.5.493 Bayesian estimation and control of detailing effort in a repeat purchase diffusion environment
Manag. Sci. Rao 34 734 1988 10.1287/mnsc.34.6.734 Forecasting with a repeat purchase diffusion model
Mark. Sci. Lenk 9 42 1990 10.1287/mksc.9.1.42 New models from old: forecasting product adoption by hierarchical Bayes procedures
Technol. Forecast. Soc. Change Mahajan 30 331 1986 10.1016/0040-1625(86)90031-4 A simple algebraic estimation procedure for innovation diffusion models of new product acceptance
Lawrence 529 1981 New Product Forecasting Applications of diffusion models: some empirical results
Ind. Manage. Data Syst. Kim 113 800 2013 10.1108/IMDS-11-2012-0414 Forecasting diffusion of innovative technology at pre-launch
Manag. Sci. Bayus 39 1319 1993 10.1287/mnsc.39.11.1319 High-definition television: assessing demand forecasts for a next generation consumer durable
Ind. Market. Manag. Choffray 15 75 1986 10.1016/0019-8501(86)90046-5 A decision-support system for evaluating sales prospects and launch strategies for new products
Manag. Sci. Lee 49 179 2003 10.1287/mnsc.49.2.179.12744 A Bayesian model for prelaunch sales forecasting of recorded music
Technol. Forecast. Soc. Change Seol 79 1217 2012 10.1016/j.techfore.2012.03.002 Demand forecasting for new media services with consideration of competitive relationships using the competitive Bass model and the theory of the niche
IMA J. Manag. Math. Goodwin 24 407 2012 10.1093/imaman/dpr025 The use of analogies in forecasting the annual sales of new electronics products
Bishop 2006 Pattern Recognition and Machine Learning
Mitchell 1997 Machine Learning
Mark. Sci. Schmittlein 1 57 1982 10.1287/mksc.1.1.57 Maximum likelihood estimation for an innovation diffusion model of new product acceptance
Mark. Sci. Srinivasan 5 169 1986 10.1287/mksc.5.2.169 Technical Note-Nonlinear least squares estimation of new product diffusion models
Manag. Sci. Heeler 26 1007 1980 10.1287/mnsc.26.10.1007 Problems in predicting new product growth for consumer durables
Putsis 263 2000 New-product Diffusion Models Estimation techniques for macro diffusion models
Technol. Forecast. Soc. Change Lee 79 1280 2012 10.1016/j.techfore.2012.04.003 Forecasting demand for a newly introduced product using reservation price data and Bayesian updating
Bass 1986 Innovation Diffusion Models of New Product Acceptance Ballinger, Cambridge The adoption of a marketing model: comments and observations
Int. J. Forecast. Lee 23 377 2007 10.1016/j.ijforecast.2007.02.006 Providing support for the use of analogies in demand forecasting tasks
Technol. Forecast. Soc. Change Ilonen 73 182 2006 10.1016/j.techfore.2004.11.005 Toward automatic forecasts for diffusion of innovations
Mark. Sci. Gatignon 8 231 1989 10.1287/mksc.8.3.231 Modeling multinational diffusion patterns: an efficient methodology
Technol. Forecast. Soc. Change Srivastava 28 325 1985 10.1016/0040-1625(85)90034-4 A multi-attribute diffusion model for forecasting the adoption of investment alternatives for consumers
Manag. Sci. Mahajan 24 1589 1978 10.1287/mnsc.24.15.1589 Innovation diffusion in a dynamic potential adopter population
Ross 2009 Introduction to Probability and Statistics for Engineers and Scientists
Hastie 2009 The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Iwerks 512 2003 Proceedings of the 29th International Conference on Very Large Data Bases (VLDB'03) Continuous k-nearest neighbor queries for continuously moving points with updates
Xiaohui 631 2005 Proceedings of the 21st International Conference on Data Engineering (ICDE 2005), Tokyo, Japan Monitoring k-nearest neighbor queries over moving objects
Mouratidis 43 2006 Proceedings of the 32nd International Conference on Very Large Data Bases Continuous nearest neighbor monitoring in road networks
Ann. Intern. Med. Baxt 115 843 1991 10.7326/0003-4819-115-11-843 Use of an artificial neural network for the diagnosis of myocardial infarction
IEEE Trans. Power Syst. Park 6 442 1991 10.1109/59.76685 Electric load forecasting using an artificial neural network
Inform. Software Tech. Heiat 44 911 2002 10.1016/S0950-5849(02)00128-3 Comparison of artificial neural network and regression models for estimating software development effort
Snyman 2005 Practical mathematical optimization: an introduction to basic optimization theory and classical and new gradient-based algorithms
SIAM Rev. Ypma 37 531 1995 10.1137/1037125 Historical development of the Newton-Raphson method
SIAM J. Appl. Math. Marquardt 11 431 1963 10.1137/0111030 An algorithm for least-squares estimation of nonlinear parameters
Stat. Comput. Smola 14 199 2004 10.1023/B:STCO.0000035301.49549.88 A tutorial on support vector regression
Breiman 1984 Classification and Regression Trees
Rasmussen 2005 Gaussian Processes for Machine Learning
Neural. Comput. Avnimelech 11 483 1999 10.1162/089976699300016737 Boosted mixture of experts: an ensemble learning scheme
Eur. J. Oper. Res. Saaty 48 9 1990 10.1016/0377-2217(90)90057-I How to make a decision: the analytic hierarchy process
Displaybank 2010 3D TV Industry Trend and Market Forecast, Speical Report May 2010, Gyeonggi-do, Korea
J. Mark. Res. Mahajan 27 37 1990 10.2307/3172549 Determination of adopter categories by using innovation diffusion models
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.