基于多源信息融合的3G手机市场预测研究
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摘要
随着手机在日常生活中应用越来越广泛,传统的2G手机已不能满足人们的需求,3G手机在这样的大环境下应运而生。而在这个新生的市场面前,有竞争也有风险,如何做好市场预测,已成为规划厂商产量和零售商销量关系的关键,决定着企业的长远发展和商家的成败。通过分析传统的市场预测,发现在实际预测中对信息的利用程度还不够完整,如何综合所得的信息,发挥信息的最大作用,具有重要的理论和实践意义。
本研究首先分析了3G手机市场的特点,介绍了传统市场预测的一些方法,分析了他们的优缺点,对信息的预处理过程做了详细的数学描述,结合信息融合发展的最新进展,将市场信息看做是传感器获得的信息,利用多源信息融合的思想,采用处理不确定问题有很大优势的证据理论融合算法,给出了基于证据理论的多源信息融合在市场预测应用中的分布式融合模型,并给出了具体的预测步骤。最后,本论文结合湖北省一年内的3G手机市场销售情况,结合了定量预测的方法,通过给出的基于信息融合的预测模型,给出了下个月的销售量。
本文很好的将主观和客观因素相结合起来,充分利用了市场信息,通过分析和比较预测结果,显示出本预测方法提高了市场预测精度。
As mobile phones in daily life is more and more extensive, traditional 2G mobile already can't satisfy people's needs, 3G phones appeared under the such circumstances. But in the new market, there is competition also has a risk. How do market forecast, has become the key to the relationship between manufacturers of production and retailer of sales, determines the long-term development of the enterprise and business success.Through the analysis of the traditional market forecast method, we can get that these methods of utilization degree of information is not complete.How to synthesize these information, play their biggest role, is a typical uncertain problems.
This research firstly analyzes the characteristics of 3G mobile-phone market, and introduces some method of traditional market forecast, analyzes the advantages and disadvantages of them, describes the detailed pretreatment process of the information, combines the latest progress in the development of information fusion, takes market information as the sensor of information, using the thought of multi-source of information fusion and D-S evidence theory fusion algorithm which handles uncertainty have great advantages,gives the distributed fusion model of multi-source information fusion that used in market forecast based on D-S evidence theory,and gives the specific forecast steps.Finally, this paper integrates the sale of 3G mobile phone market in one year of Hubei province, combines the method of quantitative prediction, based on information fusion with the prediction model, given the sale of next month, which makes full use of the market information, improve the market forecast accuracy.
The article union the subjective and objective factors, fully utilize the market information,the forecasting method improve the market forecast accuracy.
本研究首先分析了3G手机市场的特点,介绍了传统市场预测的一些方法,分析了他们的优缺点,对信息的预处理过程做了详细的数学描述,结合信息融合发展的最新进展,将市场信息看做是传感器获得的信息,利用多源信息融合的思想,采用处理不确定问题有很大优势的证据理论融合算法,给出了基于证据理论的多源信息融合在市场预测应用中的分布式融合模型,并给出了具体的预测步骤。最后,本论文结合湖北省一年内的3G手机市场销售情况,结合了定量预测的方法,通过给出的基于信息融合的预测模型,给出了下个月的销售量。
本文很好的将主观和客观因素相结合起来,充分利用了市场信息,通过分析和比较预测结果,显示出本预测方法提高了市场预测精度。
As mobile phones in daily life is more and more extensive, traditional 2G mobile already can't satisfy people's needs, 3G phones appeared under the such circumstances. But in the new market, there is competition also has a risk. How do market forecast, has become the key to the relationship between manufacturers of production and retailer of sales, determines the long-term development of the enterprise and business success.Through the analysis of the traditional market forecast method, we can get that these methods of utilization degree of information is not complete.How to synthesize these information, play their biggest role, is a typical uncertain problems.
This research firstly analyzes the characteristics of 3G mobile-phone market, and introduces some method of traditional market forecast, analyzes the advantages and disadvantages of them, describes the detailed pretreatment process of the information, combines the latest progress in the development of information fusion, takes market information as the sensor of information, using the thought of multi-source of information fusion and D-S evidence theory fusion algorithm which handles uncertainty have great advantages,gives the distributed fusion model of multi-source information fusion that used in market forecast based on D-S evidence theory,and gives the specific forecast steps.Finally, this paper integrates the sale of 3G mobile phone market in one year of Hubei province, combines the method of quantitative prediction, based on information fusion with the prediction model, given the sale of next month, which makes full use of the market information, improve the market forecast accuracy.
The article union the subjective and objective factors, fully utilize the market information,the forecasting method improve the market forecast accuracy.
引文
[1]徐国祥.统计预测与决策[M].上海:上海财经大学出版社,2005,1-36
[2]梅汝和,余名岳.市场调查和预测的应用[M].上海:上海人民出版社,1989,12-45
[3]茆诗松,王静龙,濮晓龙.高等数理统计[M].北京:高等教育出版社,1998,138-230
[4]冯忠铨.经济预测与决策[M].北京:中国财经出版社,1995,1-45
[5]何国栋.市场预测方法一百种[M].广东:广东科技出版社,1987,121-178
[6]杨曾武.统计决策原理[M].上海:上海人民出版社,1990,1-65
[7]徐国祥.管理统计学[M].上海:上海财经大学出版社,1995,213-256
[8]王学仁.实用多元统计分析[M].上海:上海科学技术出版社,1990,201-245
[9]姜仁峰,卢兵.实用预测技术[M].哈尔滨:哈尔滨船舶工程学院出版社,1993,18-49
[10]何友,王国宏等.多传感器信息融合及应用[M].北京:电子工业出版社,2007,40-45
[11]黄瑛,陶云刚等.D-S证据理论在多传感器数据融合中的应用[J].南京航空航天大学学报,1999,31 (2),172-177
[12]王江萍.基于多传感器融合信息的故障诊断[J].机械科学与技术,2000,19(6),8-16
[13]李向莉,吕建平.D-S证据理论在多传感器信息融合中的改进[M].现代电子技术,2005.16:16-18
[14]杨万海.多传感器数据融合及其应用[M].西安:西安电子科技大学出版社,2000.4,10-28
[15] Dempster A.P.A generalization of Bayesian inference[J]. Journal of the Royal Statistical Society, 1998, Series B30:205-247
[16] Thomas R. Kochtanek, Karen K. Hein. Delphi study of digital libraries.Information Processing and Management, 1999, 35: 245-254
[17] Yocky D A.Image merging and data fusion by means of the discrete two dimensional wavelet transform [J]. J Opt Soc Am A, 1995, 12(9): 1834-1841.
[18] Nunez J, Otazu X, Fors O, Prades A. Multiresolution-based image fusion with additive wavelet decomposition [J]. IEEE Transactions on Geo-science and Remote Sensing, 1999, 37(3): 1204-1211
[19] Li H, Manjunath B S, Mitra S K. Multisensor image fusion u sing the wavelet transform[J]. Graphical Models and Image Processing, 1995, 27(3):235-244.
[20]罗志增,蒋静坪.基于D-S理论的多信息融合方法及应用[J].电子学报,1999,09,4-8
[21] Xu Guo-ping.An AGO-SVM drift modelling method for a dynami-cally tuned gyroscope [J].Mess Sci Technol, 2006(17):161-167
[22] S.Makridakis,Spyros,S.C Wheelwright,and Rob J,Hyndman Forecasting Methods and Applications,3rd.ed.John wiley&Sons,Jnc,New York,1997,488-512
[23]弗雷德·R·戴维.李克宁译.战略管理[M].北京:经济科学出版社.2001,1-11
[24]张淑清,邓红,王艳玲.D-S证据理论在数据融合中的应用及改进[J].传感技术学报,2003,1(3):78—81
[25]杨露菁,余华.多源信息融合理论和应用[M].北京:北京邮电大学出版社,2006,214-228
[26] BOSTOM J R.A signal detection system based on Dempster-Shafer theory and comparison to fuzzy detection [J].IEEE Transactions on systems Man and cybenetics.C, 2000, 30(1): 45-51
[27] Kazanzides P., J.Zuhars, B.Mittelstadt and R.H.Taylor,Force Sensing and control for a surgical robot,Porc.of IEEE Int.Conf.on Robotics and Automation,1992,vl:612-617
[28] Poore A., Multi-dimensional assignment formulation of data association problems arising form multi-target and multi-sensortracking, Computational Optimize.1994, vol.3:27-57
[29] S.Prabhuetal., Artificiale ural network based or bot control.an overview Jounral of Intelligent and Robotic Systems, 1996, 15:333-365
[30] Moshe Kam, Xiaoxun Zhu, Paul Kalata, Sensor Fusion for Mobile Robot Navigation, Proceedings of the IEEE.1997, 85(1):108-119
[31] James H.Graham, Jozef M.Zurada, A neural network approach for safety and collision avoidance in robotic systems,Reliability Engineeirng and System Safety,1996,v53:27-338
[32] Libby E.W., Maybeck P.S,Sequence comparison techniques for multisensor data fusion and target recognition,IEEE Transactions on Aeorspace and Electornic Systems,1996,32(l):52-65
[33]孙奇,杨伟.D-S证据理论融合改进BP网络的短期负荷预测研究[J].继电器2007,35(7): 61-65
[34]张文修,梁怡.不确定推理原理[M].西安:西安交通大学出版社,1996,3-9
[35]方勇.证据推理应用于多源信息融合分析[J].遥感学报,2000.4(2):25-29
[36] David L.Hall, Hames Llinas. An Introduction to Multisensor Data Fusion, Proceedings of the IEEE, 1997, 85(1):6-23
[37] Sasiadek J Z,“Sensor fusion,”Annual Reviews in Control, vol.26, pp.203-228, 2002
[38] M V Srinivasan, J S Chahl, K Weber,“Robot navigation inspired by principles of insect vision,”Robotics and Autonomous System, vol.26, pp.203-216, 1999
[39] Heng Da Cheng, et al.A hierarchical Approach to Color Image Segmentation Using Homogeneity [J]. IEEE Trans. On Image Processing, 2000, 9(12):2071-2082
[40] Paul Kalata, Moshe Kam, Xiaoxun Zhu, Sensor Fusion for Mobile Robot Navigation, Proceedings of the IEEE, 1997, 85(1):18-29
[41] Wu Y G,Yang J Y,Lin K and Liu L J.On the evidence inference theory,Information Science,1996,89 (3/4):245-260
[42] Pawiak Zdzislaw, Rough set theory and its applications to data analysis, Cybernetics and System, 1998, 29(7):661-688
[43]李茹,李弼程,李斗.D-S证据理论在时-空信息融合中的应用.计算机工程与应用[J].2005.13:174-178
[44] Keller, A. Future development of electronic journals: a Delphi survey. ELECTRONIC LIBRARY, 2001, 19 (6):383-396
[2]梅汝和,余名岳.市场调查和预测的应用[M].上海:上海人民出版社,1989,12-45
[3]茆诗松,王静龙,濮晓龙.高等数理统计[M].北京:高等教育出版社,1998,138-230
[4]冯忠铨.经济预测与决策[M].北京:中国财经出版社,1995,1-45
[5]何国栋.市场预测方法一百种[M].广东:广东科技出版社,1987,121-178
[6]杨曾武.统计决策原理[M].上海:上海人民出版社,1990,1-65
[7]徐国祥.管理统计学[M].上海:上海财经大学出版社,1995,213-256
[8]王学仁.实用多元统计分析[M].上海:上海科学技术出版社,1990,201-245
[9]姜仁峰,卢兵.实用预测技术[M].哈尔滨:哈尔滨船舶工程学院出版社,1993,18-49
[10]何友,王国宏等.多传感器信息融合及应用[M].北京:电子工业出版社,2007,40-45
[11]黄瑛,陶云刚等.D-S证据理论在多传感器数据融合中的应用[J].南京航空航天大学学报,1999,31 (2),172-177
[12]王江萍.基于多传感器融合信息的故障诊断[J].机械科学与技术,2000,19(6),8-16
[13]李向莉,吕建平.D-S证据理论在多传感器信息融合中的改进[M].现代电子技术,2005.16:16-18
[14]杨万海.多传感器数据融合及其应用[M].西安:西安电子科技大学出版社,2000.4,10-28
[15] Dempster A.P.A generalization of Bayesian inference[J]. Journal of the Royal Statistical Society, 1998, Series B30:205-247
[16] Thomas R. Kochtanek, Karen K. Hein. Delphi study of digital libraries.Information Processing and Management, 1999, 35: 245-254
[17] Yocky D A.Image merging and data fusion by means of the discrete two dimensional wavelet transform [J]. J Opt Soc Am A, 1995, 12(9): 1834-1841.
[18] Nunez J, Otazu X, Fors O, Prades A. Multiresolution-based image fusion with additive wavelet decomposition [J]. IEEE Transactions on Geo-science and Remote Sensing, 1999, 37(3): 1204-1211
[19] Li H, Manjunath B S, Mitra S K. Multisensor image fusion u sing the wavelet transform[J]. Graphical Models and Image Processing, 1995, 27(3):235-244.
[20]罗志增,蒋静坪.基于D-S理论的多信息融合方法及应用[J].电子学报,1999,09,4-8
[21] Xu Guo-ping.An AGO-SVM drift modelling method for a dynami-cally tuned gyroscope [J].Mess Sci Technol, 2006(17):161-167
[22] S.Makridakis,Spyros,S.C Wheelwright,and Rob J,Hyndman Forecasting Methods and Applications,3rd.ed.John wiley&Sons,Jnc,New York,1997,488-512
[23]弗雷德·R·戴维.李克宁译.战略管理[M].北京:经济科学出版社.2001,1-11
[24]张淑清,邓红,王艳玲.D-S证据理论在数据融合中的应用及改进[J].传感技术学报,2003,1(3):78—81
[25]杨露菁,余华.多源信息融合理论和应用[M].北京:北京邮电大学出版社,2006,214-228
[26] BOSTOM J R.A signal detection system based on Dempster-Shafer theory and comparison to fuzzy detection [J].IEEE Transactions on systems Man and cybenetics.C, 2000, 30(1): 45-51
[27] Kazanzides P., J.Zuhars, B.Mittelstadt and R.H.Taylor,Force Sensing and control for a surgical robot,Porc.of IEEE Int.Conf.on Robotics and Automation,1992,vl:612-617
[28] Poore A., Multi-dimensional assignment formulation of data association problems arising form multi-target and multi-sensortracking, Computational Optimize.1994, vol.3:27-57
[29] S.Prabhuetal., Artificiale ural network based or bot control.an overview Jounral of Intelligent and Robotic Systems, 1996, 15:333-365
[30] Moshe Kam, Xiaoxun Zhu, Paul Kalata, Sensor Fusion for Mobile Robot Navigation, Proceedings of the IEEE.1997, 85(1):108-119
[31] James H.Graham, Jozef M.Zurada, A neural network approach for safety and collision avoidance in robotic systems,Reliability Engineeirng and System Safety,1996,v53:27-338
[32] Libby E.W., Maybeck P.S,Sequence comparison techniques for multisensor data fusion and target recognition,IEEE Transactions on Aeorspace and Electornic Systems,1996,32(l):52-65
[33]孙奇,杨伟.D-S证据理论融合改进BP网络的短期负荷预测研究[J].继电器2007,35(7): 61-65
[34]张文修,梁怡.不确定推理原理[M].西安:西安交通大学出版社,1996,3-9
[35]方勇.证据推理应用于多源信息融合分析[J].遥感学报,2000.4(2):25-29
[36] David L.Hall, Hames Llinas. An Introduction to Multisensor Data Fusion, Proceedings of the IEEE, 1997, 85(1):6-23
[37] Sasiadek J Z,“Sensor fusion,”Annual Reviews in Control, vol.26, pp.203-228, 2002
[38] M V Srinivasan, J S Chahl, K Weber,“Robot navigation inspired by principles of insect vision,”Robotics and Autonomous System, vol.26, pp.203-216, 1999
[39] Heng Da Cheng, et al.A hierarchical Approach to Color Image Segmentation Using Homogeneity [J]. IEEE Trans. On Image Processing, 2000, 9(12):2071-2082
[40] Paul Kalata, Moshe Kam, Xiaoxun Zhu, Sensor Fusion for Mobile Robot Navigation, Proceedings of the IEEE, 1997, 85(1):18-29
[41] Wu Y G,Yang J Y,Lin K and Liu L J.On the evidence inference theory,Information Science,1996,89 (3/4):245-260
[42] Pawiak Zdzislaw, Rough set theory and its applications to data analysis, Cybernetics and System, 1998, 29(7):661-688
[43]李茹,李弼程,李斗.D-S证据理论在时-空信息融合中的应用.计算机工程与应用[J].2005.13:174-178
[44] Keller, A. Future development of electronic journals: a Delphi survey. ELECTRONIC LIBRARY, 2001, 19 (6):383-396