Pub Date : 2009-03-01DOI: 10.30016/JGS.200903.0001
Jen-Ching Tseng
In this study, the weight clustering model, which consists of Dependency of Attributes of Rough Set (RSDA) with K-means Clustering is combined with Grey Systems theory and Rough Set (RS) theory to create an automatic stock market forecasting and portfolio selection mechanism. In our proposed approach, financial data are collected every quarter and are inputted to an GM (1, 1) predicting model to forecast the future trends of the collected data over the next quarter. Next, the forecasted data of financial statement is transformed into financial ratios using a RSDA measures and clustered by using a K-means clustering algorithm, and then supplied to a RS classified module, which selects appropriate investment stocks by adopting a set of decision-making rules. Finally, a grey relational analysis technique is applied to specify an appropriate weighting of the selected stocks to maximize the portfolio's rate of return. The validity of our proposed approach is demonstrated to use the electronic stock data extracted from the financial database maintained by the Taiwan Economic Journal (TEJ). The portfolio's results derived by using our proposed weight clustering model are compared with those portfolio's results of a conventionally clustering method. It is found that our proposed method yielded a greater average annual rate of return (23.42%) on the selected stocks from 2004 to 2006 in Taiwan stock market.
{"title":"A Hybrid RS Model for Stock Portfolio Selection Allied with Weight Clustering and Grey System Theories","authors":"Jen-Ching Tseng","doi":"10.30016/JGS.200903.0001","DOIUrl":"https://doi.org/10.30016/JGS.200903.0001","url":null,"abstract":"In this study, the weight clustering model, which consists of Dependency of Attributes of Rough Set (RSDA) with K-means Clustering is combined with Grey Systems theory and Rough Set (RS) theory to create an automatic stock market forecasting and portfolio selection mechanism. In our proposed approach, financial data are collected every quarter and are inputted to an GM (1, 1) predicting model to forecast the future trends of the collected data over the next quarter. Next, the forecasted data of financial statement is transformed into financial ratios using a RSDA measures and clustered by using a K-means clustering algorithm, and then supplied to a RS classified module, which selects appropriate investment stocks by adopting a set of decision-making rules. Finally, a grey relational analysis technique is applied to specify an appropriate weighting of the selected stocks to maximize the portfolio's rate of return. The validity of our proposed approach is demonstrated to use the electronic stock data extracted from the financial database maintained by the Taiwan Economic Journal (TEJ). The portfolio's results derived by using our proposed weight clustering model are compared with those portfolio's results of a conventionally clustering method. It is found that our proposed method yielded a greater average annual rate of return (23.42%) on the selected stocks from 2004 to 2006 in Taiwan stock market.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"12 1","pages":"1-8"},"PeriodicalIF":1.6,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70057295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-03-01DOI: 10.30016/JGS.200903.0007
C. Kung, Tzung-Ming Yan, Chih-Sung Lai
This study used grey relational analysis and LISREL method to analyze the relationships among service quality, customer satisfaction and customer loyalty, the mobile communications industry in Taiwan was the subject. The study results show that are the analyzed results by grey relational analysis, LISREL model are similar, which indicates the grey relational analysis only requires small sample size with its smaller data to obtain an acceptably fine solution. The study also finds that service quality is correlatively positive to customer satisfaction and the customer satisfaction to customer loyalty. Among them, the responsiveness and empathy factors have positively influential consequences on customer satisfaction and customer loyalty. However, the relationship between total service quality and customer loyalty are weak.
{"title":"Analyzing Service Quality in the Mobile Communications Industry-A Comparison between GRA and LISREL Methods","authors":"C. Kung, Tzung-Ming Yan, Chih-Sung Lai","doi":"10.30016/JGS.200903.0007","DOIUrl":"https://doi.org/10.30016/JGS.200903.0007","url":null,"abstract":"This study used grey relational analysis and LISREL method to analyze the relationships among service quality, customer satisfaction and customer loyalty, the mobile communications industry in Taiwan was the subject. The study results show that are the analyzed results by grey relational analysis, LISREL model are similar, which indicates the grey relational analysis only requires small sample size with its smaller data to obtain an acceptably fine solution. The study also finds that service quality is correlatively positive to customer satisfaction and the customer satisfaction to customer loyalty. Among them, the responsiveness and empathy factors have positively influential consequences on customer satisfaction and customer loyalty. However, the relationship between total service quality and customer loyalty are weak.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"12 1","pages":"49-58"},"PeriodicalIF":1.6,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70057454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-03-01DOI: 10.30016/JGS.200903.0002
Xinhai Kong, Yong Wei
Based on the grey differential equation of DGM (2, 1), its Connotation expression is derived in this paper, and shows that DGM (2, 1) is a non-homogeneous exponential type. Further, by comparing the connotation expression with the solution of the whitenization equation, we find that the solution is inconsistent with the connotation expression, and the differential restored value is also inconsistent with the inverse-accumulating restored value. So the optimized DGM (2, 1) is presented, which has the white exponential superposition. Some examples also show that the optimized model has a high simulation precision.
{"title":"Optimization of DGM (2, 1)","authors":"Xinhai Kong, Yong Wei","doi":"10.30016/JGS.200903.0002","DOIUrl":"https://doi.org/10.30016/JGS.200903.0002","url":null,"abstract":"Based on the grey differential equation of DGM (2, 1), its Connotation expression is derived in this paper, and shows that DGM (2, 1) is a non-homogeneous exponential type. Further, by comparing the connotation expression with the solution of the whitenization equation, we find that the solution is inconsistent with the connotation expression, and the differential restored value is also inconsistent with the inverse-accumulating restored value. So the optimized DGM (2, 1) is presented, which has the white exponential superposition. Some examples also show that the optimized model has a high simulation precision.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"12 1","pages":"9-13"},"PeriodicalIF":1.6,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70057308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The grey forecasting model, GM (1, 1), with the property of processing with a minimum of data, has been successfully applied in various fields. However, it has been discovered that different errors may be directly induced by different alpha levels in predicted operations. Accordingly, the parameter α plays an important role on forecasting. Thus, how to search for the optimal setting of parameter α is a valuable work. In this paper, Genetic Algorithm (GA) method has be applied in GM (1, 1) model for handling this problem. We present two illustrative examples to compare between Deng's method and our revised method. These results are useful in that they diminish the margin of error.
{"title":"Optimal Alpha Level Setting in GM (1, 1) Model Based on Genetic Algorithm","authors":"Kuo-Chen Hung, Chia-Yi Chien, Kuo-Jung Wu, Fu-Yuan Hsu","doi":"10.30016/JGS.200903.0004","DOIUrl":"https://doi.org/10.30016/JGS.200903.0004","url":null,"abstract":"The grey forecasting model, GM (1, 1), with the property of processing with a minimum of data, has been successfully applied in various fields. However, it has been discovered that different errors may be directly induced by different alpha levels in predicted operations. Accordingly, the parameter α plays an important role on forecasting. Thus, how to search for the optimal setting of parameter α is a valuable work. In this paper, Genetic Algorithm (GA) method has be applied in GM (1, 1) model for handling this problem. We present two illustrative examples to compare between Deng's method and our revised method. These results are useful in that they diminish the margin of error.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"12 1","pages":"23-31"},"PeriodicalIF":1.6,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70057046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-03-01DOI: 10.30016/JGS.200903.0006
S. Wan
Aimed at the object recognition problem in which the characteristic values of object types and observations of sensors are in the form of interval numbers, a new method based on interval number for multi-sensor data fusion on object-level is proposed by grey relational analysis. The method defines the distance between the two interval numbers, obtains the distance matrix and grey relational matrix between all object types and unknown object. After solving the optimization problem of maximizing the deviation for all attributes, the weights of the attributes are derived. Thus, the result of recognition for the unknown object is given by the grey relational grade. This method can avoid the subjectivity of selecting attributes weights and improve the objectivity and accuracy of object recognition. The simulated example verifies the feasibility and practicability of the proposed method.
{"title":"Method Based on Interval Number for Multi-sensor Information Fusion on Object-level","authors":"S. Wan","doi":"10.30016/JGS.200903.0006","DOIUrl":"https://doi.org/10.30016/JGS.200903.0006","url":null,"abstract":"Aimed at the object recognition problem in which the characteristic values of object types and observations of sensors are in the form of interval numbers, a new method based on interval number for multi-sensor data fusion on object-level is proposed by grey relational analysis. The method defines the distance between the two interval numbers, obtains the distance matrix and grey relational matrix between all object types and unknown object. After solving the optimization problem of maximizing the deviation for all attributes, the weights of the attributes are derived. Thus, the result of recognition for the unknown object is given by the grey relational grade. This method can avoid the subjectivity of selecting attributes weights and improve the objectivity and accuracy of object recognition. The simulated example verifies the feasibility and practicability of the proposed method.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"12 1","pages":"41-47"},"PeriodicalIF":1.6,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70057266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-12-01DOI: 10.30016/JGS.200812.0003
T. Hsu
For years, 23 million persons have already become a familiar term among Taiwanese people. Even all-political figures are fond of mentioning ”the public opinions of 23 million persons” which causes the misunderstanding of the population of Taiwan has already reached 23 million persons. In fact, after the population of Taiwan and Fukien area reached 20 million persons in April, 1989, the population growth started to drop due to the slowly decline of birth rate. From 1989 to 1999, about one million persons every five years until 1999, the population were 22 million persons. Then, the population reached 23 million persons after nine years. We know that population policy is one of the essential policies of developed countries, the sum of population acts as a main reference for the government to conduct all kinds of policies. Additionally, the limitation of human being's living source will cause the impact of whole ecology balance, based on this phenomenon, this paper tended to set up a saturated analysis model of the population of Taiwan (including Kinmen and Matsu) by utilizing grey Verhulst and GM (1, 1) method. It built and predicts the population number and conducts a case analysis toward the saturation value of reaching 23 million persons on Taiwan.
{"title":"The Predication of 23 Million Persons on Taiwan and Fukien via GM Model","authors":"T. Hsu","doi":"10.30016/JGS.200812.0003","DOIUrl":"https://doi.org/10.30016/JGS.200812.0003","url":null,"abstract":"For years, 23 million persons have already become a familiar term among Taiwanese people. Even all-political figures are fond of mentioning ”the public opinions of 23 million persons” which causes the misunderstanding of the population of Taiwan has already reached 23 million persons. In fact, after the population of Taiwan and Fukien area reached 20 million persons in April, 1989, the population growth started to drop due to the slowly decline of birth rate. From 1989 to 1999, about one million persons every five years until 1999, the population were 22 million persons. Then, the population reached 23 million persons after nine years. We know that population policy is one of the essential policies of developed countries, the sum of population acts as a main reference for the government to conduct all kinds of policies. Additionally, the limitation of human being's living source will cause the impact of whole ecology balance, based on this phenomenon, this paper tended to set up a saturated analysis model of the population of Taiwan (including Kinmen and Matsu) by utilizing grey Verhulst and GM (1, 1) method. It built and predicts the population number and conducts a case analysis toward the saturation value of reaching 23 million persons on Taiwan.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"11 1","pages":"187-192"},"PeriodicalIF":1.6,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70057284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-09-01DOI: 10.30016/JGS.200809.0007
John H. Wu, Chia-Yi Chien, Hsiao-Mei Liu, Furong Chang
The essences of traditional grey forecasting theory are background value and class ratio. In traditional GM(1,1) model, the background value is an average one which is restricted on a point and class ratio is too limited to be accepted that may only be agreeable to monotone increasing or decreasing cases. Therefore, a linear assumption and an optimal alpha are introduced for background value. Besides, base on spatial perspective, an error analysis will be constructed to improve comprehension of this model. A comparison of example indicates that the modified approach is probably to reduce forecasting error by RMSE evaluation. Besides, this is the first part of series paper and gradual modifications will also be proposed to enhance applications of GM(1,1) in the future.
{"title":"Restudy on Traditional Grey Forecasting Theory of GM (1,1)-I","authors":"John H. Wu, Chia-Yi Chien, Hsiao-Mei Liu, Furong Chang","doi":"10.30016/JGS.200809.0007","DOIUrl":"https://doi.org/10.30016/JGS.200809.0007","url":null,"abstract":"The essences of traditional grey forecasting theory are background value and class ratio. In traditional GM(1,1) model, the background value is an average one which is restricted on a point and class ratio is too limited to be accepted that may only be agreeable to monotone increasing or decreasing cases. Therefore, a linear assumption and an optimal alpha are introduced for background value. Besides, base on spatial perspective, an error analysis will be constructed to improve comprehension of this model. A comparison of example indicates that the modified approach is probably to reduce forecasting error by RMSE evaluation. Besides, this is the first part of series paper and gradual modifications will also be proposed to enhance applications of GM(1,1) in the future.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"11 1","pages":"165-172"},"PeriodicalIF":1.6,"publicationDate":"2008-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70057145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-06-01DOI: 10.30016/JGS.200806.0004
C. Chung, Ho‐Hsien Chen, P. Hsieh
The fermentation process in general is a kind of non-linear and time-variant system. It is necessary to determine optimal conditions in the fermentation process with multiple performance characteristics In this study, a Grey-based Taguchi Method was applied to find the optimal conditions of medium composition to enhance the production of biomass, monacolin K, pigments synthesis, and to reduce the yield of citrinin, metabolites in the Monascus purpureus fermentation process. In particular, the amount of citrinin would cause potential adverse effects on human health and depends on the quality and control of the fermentation process. The results show that the optimal conditions under these multiple performance requirements for Monascus purpureus fermentation are: A4 (1% whole wheat flour), B3 (1% pepton), C1 (1% olive oil), D4 (0.01% KH2PO4), E1 (pH=3). That combination can yield iomass at 2.065 g/100mL, monacolin K at 150.052 ppm, yellow pigment (OD400) at 4.082 ppm, orange pigment (OD460) at 4.231 ppm, red pigment (OD400) at 8.105 ppm, respectively, with ctirinin yield reduced to 0.082 ppb. The optimal combination obtained from this study could be a reference for production line fermentation processing.
{"title":"Optimization of the Monascus purpureus Fermentation Process Based on Multiple Performance Characteristics","authors":"C. Chung, Ho‐Hsien Chen, P. Hsieh","doi":"10.30016/JGS.200806.0004","DOIUrl":"https://doi.org/10.30016/JGS.200806.0004","url":null,"abstract":"The fermentation process in general is a kind of non-linear and time-variant system. It is necessary to determine optimal conditions in the fermentation process with multiple performance characteristics In this study, a Grey-based Taguchi Method was applied to find the optimal conditions of medium composition to enhance the production of biomass, monacolin K, pigments synthesis, and to reduce the yield of citrinin, metabolites in the Monascus purpureus fermentation process. In particular, the amount of citrinin would cause potential adverse effects on human health and depends on the quality and control of the fermentation process. The results show that the optimal conditions under these multiple performance requirements for Monascus purpureus fermentation are: A4 (1% whole wheat flour), B3 (1% pepton), C1 (1% olive oil), D4 (0.01% KH2PO4), E1 (pH=3). That combination can yield iomass at 2.065 g/100mL, monacolin K at 150.052 ppm, yellow pigment (OD400) at 4.082 ppm, orange pigment (OD460) at 4.231 ppm, red pigment (OD400) at 8.105 ppm, respectively, with ctirinin yield reduced to 0.082 ppb. The optimal combination obtained from this study could be a reference for production line fermentation processing.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"11 1","pages":"85-95"},"PeriodicalIF":1.6,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70056651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-06-01DOI: 10.30016/JGS.200806.0003
GuoDong Li, S. Masuda, D. Yamaguchi, Masayuki Hayashi, M. Nagai
This paper proposes a new grey-based dynamic model (GM) to realize the predictions and analyses for the development trends of GDP, population and primary energy consumption from year 2004 to 2010 based on data from 1995 to 2003. The proposal GM is obtained by the following procedures: First, statistical method of linear regression is integrated into GM to enhance prediction capability. Second, residual error modification with Markov-chain sign estimation further improves the accuracy. Finally, we verified the effectiveness of proposal model through experiment. We also discussed the relationship among the development trends of GDP, population and energy consumption for the future. The results of experiment are simulated with Matlab.
{"title":"A Study on the Development Trends of GDP, Population and Primary Energy Consumption by Grey-based Dynamic Mode","authors":"GuoDong Li, S. Masuda, D. Yamaguchi, Masayuki Hayashi, M. Nagai","doi":"10.30016/JGS.200806.0003","DOIUrl":"https://doi.org/10.30016/JGS.200806.0003","url":null,"abstract":"This paper proposes a new grey-based dynamic model (GM) to realize the predictions and analyses for the development trends of GDP, population and primary energy consumption from year 2004 to 2010 based on data from 1995 to 2003. The proposal GM is obtained by the following procedures: First, statistical method of linear regression is integrated into GM to enhance prediction capability. Second, residual error modification with Markov-chain sign estimation further improves the accuracy. Finally, we verified the effectiveness of proposal model through experiment. We also discussed the relationship among the development trends of GDP, population and energy consumption for the future. The results of experiment are simulated with Matlab.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"11 1","pages":"73-84"},"PeriodicalIF":1.6,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70056531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-06-01DOI: 10.30016/JGS.200806.0007
Zheng-peng Wu, Sifeng Liu, Chuanmin Mi
Based on the present theories of buffer operators, this paper proposed a kind of buffer operator, which all has the universality and practicability. We have proved it to be strengthening buffer operator.
{"title":"Study on the Sequence of Strengthening Buffer Operator Based on the Strictly Monotonic Function","authors":"Zheng-peng Wu, Sifeng Liu, Chuanmin Mi","doi":"10.30016/JGS.200806.0007","DOIUrl":"https://doi.org/10.30016/JGS.200806.0007","url":null,"abstract":"Based on the present theories of buffer operators, this paper proposed a kind of buffer operator, which all has the universality and practicability. We have proved it to be strengthening buffer operator.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"11 1","pages":"113-118"},"PeriodicalIF":1.6,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70056728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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