管理工程学报
管理工程學報
관리공정학보
Journal of Industrial Engineering and Engineering Management
2011年
2期
173~177
,共null页
经济波动 转折点 动态贝叶斯网络 分类器 隐藏变量
經濟波動 轉摺點 動態貝葉斯網絡 分類器 隱藏變量
경제파동 전절점 동태패협사망락 분류기 은장변량
economic fluctuation; turning point; dynamic Bayesian network; classifier; hidden variable
针对现有的经济周期波动转折点预测方法侧重静态函数依赖,或者强调动态序列的时间传递,不能将两方面信息有机结合的情况,给出了经济周期波动转折点预测的动态朴素贝叶斯网络分类器模型,并在此基础上,通过增加隐藏变量层建立了层次动态朴素贝叶斯网络分类器模型,该模型更加灵活、实用和可靠,可广泛用于网络时间序列的预测。
針對現有的經濟週期波動轉摺點預測方法側重靜態函數依賴,或者彊調動態序列的時間傳遞,不能將兩方麵信息有機結閤的情況,給齣瞭經濟週期波動轉摺點預測的動態樸素貝葉斯網絡分類器模型,併在此基礎上,通過增加隱藏變量層建立瞭層次動態樸素貝葉斯網絡分類器模型,該模型更加靈活、實用和可靠,可廣汎用于網絡時間序列的預測。
침대현유적경제주기파동전절점예측방법측중정태함수의뢰,혹자강조동태서렬적시간전체,불능장량방면신식유궤결합적정황,급출료경제주기파동전절점예측적동태박소패협사망락분류기모형,병재차기출상,통과증가은장변량층건립료층차동태박소패협사망락분류기모형,해모형경가령활、실용화가고,가엄범용우망락시간서렬적예측。
Macroeconomics studies primarily explore the rules of turning points of economic cycles by using the prediction methods of function fitting and time series.However,these two methods are primarily based on the concept of static time series and do not consider dynamic time series.A dynamic Bayesian network combines static and dynamic time series at a given time frame.The network adds the time series function to the existing Bayesian network features,including versatility,efficiency and openness.Dynamic Bayesian networks have been applied to causal analysis,the prediction of multi-variables network time series,and other areas.The dynamic Bayesian network used for prediction is called dynamic Bayesian network classifier.Dynamic naive Bayesian network(DNBN) classifier can be adopted for dynamic prediction.This kind of classifier has the advantages of simplicity and high efficiency.However,on the assumption of conditional independence between attribute variables,the prediction accuracy will be decreased when there is a strong conditional dependency between variables.Gaussian distribution or Gaussian kernel distribution is used for continuous attributes.A large difference exists between actual distribution and Gaussian distribution.Gaussian kernel distribution often has the over-fitting problem that can decrease the classifier's generalization ability.Hidden variables play important roles in time series prediction.The performance of the DNBN classifier can be improved by adding a hidden variable layer.This addition can help establish a dynamic hierarchical Bayesian network(abbrevd.HDNBN) classifier.In a HDNBN classifier,hidden variables have two main functions:(1) they can use a Mixed Gaussian distribution to replace Gaussian distribution(Mix Gaussian distribution can estimate any distribution of continuous variables).This replacement can increase the reliability of the conditional density estimation and regulate the fitting degree of classifiers by the means and dimensions of hidden variables;and(2) it can aggregate the degree of dependence between attributes.Improved classifiers can be applied to the prediction of network time series in more flexible,effective,reliable and practical manners.