This paper develops and compares different models to forecast new product sales data with increasing sales trend and multiple predictor inputs. In order to analyze new product with increasing sales trend, we developed and evaluated multiple time series forecasting methods, including Exponential Smoothing model, Holt's Linear model, ARMA model, and ARMA wit linear trend models. Furthermore, we created multiple Causal Factor Forecasting models to incorporate various dependent input factors such as sale person's quotes, product pricing, product seasonality factors, to further reduce forecasting error. We analyzed original data regression model, trend and residual regression model, and ARMAV wit linear trend model to consider input factors. We discovered that ARMAV wit linear trend model gives best forecasting accuracy and lowest RSS (Residual Sum of Square). In conclusion, ARMAV with linear trend method is the best benchmark model to forecast sales data for new product with trend and with sales person's inputs.
{"title":"Data Mining Algorithms and Statistical Analysis for Sales Data Forecast","authors":"Lin Wu, Jinyao Yan, Y. Fan","doi":"10.1109/CSO.2012.132","DOIUrl":"https://doi.org/10.1109/CSO.2012.132","url":null,"abstract":"This paper develops and compares different models to forecast new product sales data with increasing sales trend and multiple predictor inputs. In order to analyze new product with increasing sales trend, we developed and evaluated multiple time series forecasting methods, including Exponential Smoothing model, Holt's Linear model, ARMA model, and ARMA wit linear trend models. Furthermore, we created multiple Causal Factor Forecasting models to incorporate various dependent input factors such as sale person's quotes, product pricing, product seasonality factors, to further reduce forecasting error. We analyzed original data regression model, trend and residual regression model, and ARMAV wit linear trend model to consider input factors. We discovered that ARMAV wit linear trend model gives best forecasting accuracy and lowest RSS (Residual Sum of Square). In conclusion, ARMAV with linear trend method is the best benchmark model to forecast sales data for new product with trend and with sales person's inputs.","PeriodicalId":170543,"journal":{"name":"2012 Fifth International Joint Conference on Computational Sciences and Optimization","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125013170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wang Jing, Zhou Yongsheng, Yang Hao-xiong, Zhan-Ju Hao
Most optimization problems in real life are multi-objective optimization problems. The difficulity of multi-objective programming lies in the fact that the objectives are in conflict with each other and an improvement of one objective may lead to the reduction of other objectives. While achieving the global optimal in all objective at the same time is impossible. we use particle swarm optimization to improve the multi-objective patero solution and get the multi-objective trade-off patero optimal solutions. Numerical experiments show that our algorithms are effective, we can get multi-objective patero solutions set and multi-objective trade-offs patero optimal solution at the same time.
{"title":"A Trade-off Pareto Solution Algorithm for Multi-objective Optimization","authors":"Wang Jing, Zhou Yongsheng, Yang Hao-xiong, Zhan-Ju Hao","doi":"10.1109/CSO.2012.34","DOIUrl":"https://doi.org/10.1109/CSO.2012.34","url":null,"abstract":"Most optimization problems in real life are multi-objective optimization problems. The difficulity of multi-objective programming lies in the fact that the objectives are in conflict with each other and an improvement of one objective may lead to the reduction of other objectives. While achieving the global optimal in all objective at the same time is impossible. we use particle swarm optimization to improve the multi-objective patero solution and get the multi-objective trade-off patero optimal solutions. Numerical experiments show that our algorithms are effective, we can get multi-objective patero solutions set and multi-objective trade-offs patero optimal solution at the same time.","PeriodicalId":170543,"journal":{"name":"2012 Fifth International Joint Conference on Computational Sciences and Optimization","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123240575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, a parameter-free filled function for global optimization is developed, and some theory properties of the function is discussed. An optimization algorithm for article[7] is modified. Several examples and the numerical results show that the form filled function is feasibility and efficiency.
{"title":"A New Parameter-Free Filled Function for Global Optimization","authors":"Cuixia Xu, Aifen Feng, Zhiyong Huang","doi":"10.1109/CSO.2012.72","DOIUrl":"https://doi.org/10.1109/CSO.2012.72","url":null,"abstract":"In this paper, a parameter-free filled function for global optimization is developed, and some theory properties of the function is discussed. An optimization algorithm for article[7] is modified. Several examples and the numerical results show that the form filled function is feasibility and efficiency.","PeriodicalId":170543,"journal":{"name":"2012 Fifth International Joint Conference on Computational Sciences and Optimization","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114156566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to provide marketing support for online shop owner, this paper describes how to analyze historical data of online shop customer behaviors by data mining, and establishes logistic predictive modeling by SAS, then predicts whether customers purchase a Tablet PC online shop. Practice has proved that data mining techniques and predictive regression modeling can be effectively integrated in the application system, and the predictive modeling can predict the possibility of customers to buy goods. Also the predictive modeling has usability and availability.
{"title":"Research and Implementation of Predictive Modeling Based on Logistic Regression Modeling: About Possibility of Customer to Buy a Tablet PC","authors":"Weina Zhang, Shuang Feng, Hua Li","doi":"10.1109/CSO.2012.146","DOIUrl":"https://doi.org/10.1109/CSO.2012.146","url":null,"abstract":"In order to provide marketing support for online shop owner, this paper describes how to analyze historical data of online shop customer behaviors by data mining, and establishes logistic predictive modeling by SAS, then predicts whether customers purchase a Tablet PC online shop. Practice has proved that data mining techniques and predictive regression modeling can be effectively integrated in the application system, and the predictive modeling can predict the possibility of customers to buy goods. Also the predictive modeling has usability and availability.","PeriodicalId":170543,"journal":{"name":"2012 Fifth International Joint Conference on Computational Sciences and Optimization","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128481420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Haoxiong Yang, Ying Guo, Yongsheng Zhou, Mingke He
In order to predict the Beijing urban distribution amount, a grey theory model is used in this paper. Based on the former study and the current situation of Beijing urban distribution system, the paper used gray theory model GM (1, 1) and qualitative analysis to predict the amount by using the samples of goods classification. Empirical results show that the freight of primary industry of Beijing remains generally stable, but some products increase. The freight of second industry go up and down. the freight of tertiary industry also increases. The gray prediction model can simulate and predict the scale and development trend of Beijing road freight, but it is needed to consider the new development trend.
{"title":"The Predictions of Beijing Urban Distribution Amount Using Grey Model and Industrial Structure Analysis","authors":"Haoxiong Yang, Ying Guo, Yongsheng Zhou, Mingke He","doi":"10.1109/CSO.2012.47","DOIUrl":"https://doi.org/10.1109/CSO.2012.47","url":null,"abstract":"In order to predict the Beijing urban distribution amount, a grey theory model is used in this paper. Based on the former study and the current situation of Beijing urban distribution system, the paper used gray theory model GM (1, 1) and qualitative analysis to predict the amount by using the samples of goods classification. Empirical results show that the freight of primary industry of Beijing remains generally stable, but some products increase. The freight of second industry go up and down. the freight of tertiary industry also increases. The gray prediction model can simulate and predict the scale and development trend of Beijing road freight, but it is needed to consider the new development trend.","PeriodicalId":170543,"journal":{"name":"2012 Fifth International Joint Conference on Computational Sciences and Optimization","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130335747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Web content extraction is actually the process of transforming web unstructured information into structured information. Knowledge base has the advantages of ordering information and knowledge, also be used conveniently. So it's convenient to retrieve information and knowledge, and it makes base for effective use. Knowledge base will speed up the knowledge and the flow of information and make for knowledge sharing and communication. This paper puts forward a web information extraction method which is based on the knowledge base. Experiment results show that the method has greatly increased efficiency and accuracy of the web information extraction.
{"title":"The Content Extraction Method of Webpage Information Based on Knowledge Base","authors":"Guowei Chen, Pengzhou Zhang","doi":"10.1109/CSO.2012.142","DOIUrl":"https://doi.org/10.1109/CSO.2012.142","url":null,"abstract":"Web content extraction is actually the process of transforming web unstructured information into structured information. Knowledge base has the advantages of ordering information and knowledge, also be used conveniently. So it's convenient to retrieve information and knowledge, and it makes base for effective use. Knowledge base will speed up the knowledge and the flow of information and make for knowledge sharing and communication. This paper puts forward a web information extraction method which is based on the knowledge base. Experiment results show that the method has greatly increased efficiency and accuracy of the web information extraction.","PeriodicalId":170543,"journal":{"name":"2012 Fifth International Joint Conference on Computational Sciences and Optimization","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126796793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Through idealized experiments, this study is to test the performance of multi-grid three-dimensional variantional (3D-Var) on three-dimensional (3D) Doppler radar radial velocity data assimilation, and to what degree the 3D Doppler radar radial velocity can improve the conventional (in situ) wind observation analysis. A two-scale idealized wind field is constructed, and then random-distributed conventional wind data and 3D Doppler radar radial velocity data are generated based on this idealized wind field. By assimilating these data and comparing the analyses with the true idealized circulation field, the multi-grid 3D-Var performance in 3D Doppler radar radial velocity analysis is evaluated. The effects of weak constraint and strong constraint on the multi-grid 3D-Var analyses are also presented. Results show that the 3D Doppler radar radial velocity data do provide additional useful information especially in the sparse distributed conventional observation situation, and the multi-grid 3D-Var with strong constraint can make better analyses on not only horizontal but also vertical velocity.
{"title":"Multi-grid Analysis of the Three-Dimensional Doppler Radar Radial Velocity: Idealized Cases Study","authors":"Wei Li, Yuanfu Xie","doi":"10.1109/CSO.2012.183","DOIUrl":"https://doi.org/10.1109/CSO.2012.183","url":null,"abstract":"Through idealized experiments, this study is to test the performance of multi-grid three-dimensional variantional (3D-Var) on three-dimensional (3D) Doppler radar radial velocity data assimilation, and to what degree the 3D Doppler radar radial velocity can improve the conventional (in situ) wind observation analysis. A two-scale idealized wind field is constructed, and then random-distributed conventional wind data and 3D Doppler radar radial velocity data are generated based on this idealized wind field. By assimilating these data and comparing the analyses with the true idealized circulation field, the multi-grid 3D-Var performance in 3D Doppler radar radial velocity analysis is evaluated. The effects of weak constraint and strong constraint on the multi-grid 3D-Var analyses are also presented. Results show that the 3D Doppler radar radial velocity data do provide additional useful information especially in the sparse distributed conventional observation situation, and the multi-grid 3D-Var with strong constraint can make better analyses on not only horizontal but also vertical velocity.","PeriodicalId":170543,"journal":{"name":"2012 Fifth International Joint Conference on Computational Sciences and Optimization","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114720015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we use a 3-piecewise linear NCP function and propose a filter trust region method for constrained nonlinear optimization problems. based on the confrence [8], we replace the nonlinear unreasonable NCP function to the linear reasonable NCP function, and correct the filter in violation constrained function with 3-piecewise linear NCP function, At the same time prove that the filter method with global convergence.
{"title":"Trust-region Filter Method with 3-piecewise Linear NCP Function","authors":"Min Zhou, Y. Shang, Rufeng Wang","doi":"10.1109/CSO.2012.76","DOIUrl":"https://doi.org/10.1109/CSO.2012.76","url":null,"abstract":"In this paper, we use a 3-piecewise linear NCP function and propose a filter trust region method for constrained nonlinear optimization problems. based on the confrence [8], we replace the nonlinear unreasonable NCP function to the linear reasonable NCP function, and correct the filter in violation constrained function with 3-piecewise linear NCP function, At the same time prove that the filter method with global convergence.","PeriodicalId":170543,"journal":{"name":"2012 Fifth International Joint Conference on Computational Sciences and Optimization","volume":"25 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124494386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The aim of this study is to find a model to forecast China's carbon emission using the neural network. Some variables, such as GDP, export, investment in fixed assets and population, are considered. First, the neural network is constructed. Then, considering the effect of sub prime mortgage crisis and European debt crisis, we forecast the emissions in the next 10 years through the network. We find that emissions may be approaching the turning point. At last, the possible reasons are given.
{"title":"Carbon Emissions Modeling of China Using Neural Network","authors":"Peng Liu, Guoxing Zhang, Xutao Zhang, Sujie Cheng","doi":"10.1109/CSO.2012.155","DOIUrl":"https://doi.org/10.1109/CSO.2012.155","url":null,"abstract":"The aim of this study is to find a model to forecast China's carbon emission using the neural network. Some variables, such as GDP, export, investment in fixed assets and population, are considered. First, the neural network is constructed. Then, considering the effect of sub prime mortgage crisis and European debt crisis, we forecast the emissions in the next 10 years through the network. We find that emissions may be approaching the turning point. At last, the possible reasons are given.","PeriodicalId":170543,"journal":{"name":"2012 Fifth International Joint Conference on Computational Sciences and Optimization","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128080430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to solve ill-conditioned problem more efficiently, a new method called Adaptive Regularization Method based on Normal Operator(ARMNO) is proposed. By analyzing weakness of the existing adaptive regularization method, we gave a new regularization strategy for ARMNO. Property shows that ARMNO has stronger regularity than Tikhonov regularization method. For illustration, a measured GPS example is utilized to show ARMNO has higher accuracy than the several commonly used estimation methods, it can be concluded that ARMNO has better results for the solvers of serious ill-conditioned problems.
{"title":"An Adaptive Regularization Method for Ill-conditioned Problem","authors":"Jimin Liu, Xiushan Lu, Fanwei Meng","doi":"10.1109/CSO.2012.18","DOIUrl":"https://doi.org/10.1109/CSO.2012.18","url":null,"abstract":"In order to solve ill-conditioned problem more efficiently, a new method called Adaptive Regularization Method based on Normal Operator(ARMNO) is proposed. By analyzing weakness of the existing adaptive regularization method, we gave a new regularization strategy for ARMNO. Property shows that ARMNO has stronger regularity than Tikhonov regularization method. For illustration, a measured GPS example is utilized to show ARMNO has higher accuracy than the several commonly used estimation methods, it can be concluded that ARMNO has better results for the solvers of serious ill-conditioned problems.","PeriodicalId":170543,"journal":{"name":"2012 Fifth International Joint Conference on Computational Sciences and Optimization","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127917460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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