Fuzzy consecutive-k-out-of-n:F system reliability

In this paper, we propose a new method to analyze fuzzy consecutive-k-out-of-n:F system reliability using fuzzy GERT. The triangular fuzzy numbers are used to fuzzify probabilities of the consecutive-k-out-of-n:F system and the interval arithmetic, α-cuts and an index of optimism λ are applied to compute fuzzy consecutive-k-out-of-n:F system reliability on fuzzy the GERT network. Futhermore, we can obtain all computation results by “MATHEMATICA” package.     

Modeling fitting-function-based fuzzy time series patterns for evolving stock index forecasting

Fuzzy time series models that have been developed have been widely applied to many applications of forecasting future stock prices or weighted indexes in the financial field. Three interesting problems have been identified in relation to the associated time series methods, as follows: (1) conventional time series models that consider single variables on associated problems only, (2) fuzzy time series models that determine the interval length of the linguistic values subjectively, and (3) selected variables that depend on personal experience and opinion subjectively. In light of the above limitations, this study constitutes a hybrid seven-step procedure that proposes three integrated fuzzy time series models that are based on fitting functions to forecast weighted indexes of the stock market. First, the proposed models employ Pearson correlation coefficients to objectively select important technical indicators. Second, this study utilizes an objective algorithm to determine the lower bound and upper bound of the universe of discourse automatically. Third, the proposed models use the spread-partition algorithm to automatically determine linguistic intervals. Finally, they combine the transformed variables to build three fuzzy time series models using the criterion of the minimal root mean square error (RMSE). Furthermore, this study provides all of the necessary justifying information for using a linear process to select the inputs for the given non-linear data. To further evaluate the performance of the proposed models, the transaction records of TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) and HSI (Hang Seng Indexes) from 1998/01/03 to 2006/12/31 are used to illustrate the methodology with two experimental data sets. Chen’s (Fuzzy Sets Syst. 81:311–319, 1996) model, Yu’s (Physica A 349:609–624, 2005) model, support vector regression (SVR), and partial least square regression (PLSR) are used as models to be compared with the proposed model when given the same data sets. The analytical results show that the proposed models outperform the listed models under the evaluation criteria of the RMSE (in contrast to the forecasting accuracy) for forecasting a weighted stock index in both the Taiwan and Hong Kong stock markets.     

OWA rough set model for forecasting the revenues growth rate of the electronic industry

Business operation performance is related to corporation profitability and directly affects the choices of investment in the stock market. This paper proposes a hybrid method, which combines the ordered weighted averaging (OWA) operator and rough set theory after an attribute selection procedure to deal with multi-attribute forecasting problems with respect to revenue growth rate of the electronic industry. In the attribute selection step, four most-important attributes within 12 attributes collected from related literature are determined via five attribute selection methods as the input of the following procedure of the proposed method. The OWA operator can adjust the weight of an attribute based on the situation of a decision-maker and aggregate different attribute values into a single aggregated value of each instance, and then the single aggregated values are utilized to generate classification rules by rough set for forecasting operation performance.To verify the proposed method, this research collects the financial data of 629 electronic firms for public companies listed in the TSE (Taiwan Stock Exchange) and OTC (Over-the-Counter) market in 2004 and 2005 to forecast the revenue growth rate. The results show that the proposed method outperforms the listing methods.     

Volatility model based on multi-stock index for TAIEX forecasting

Conventional time series models have been applied to handle many forecasting problems, such as financial, economic and weather forecasting. In stock markets, correct stock predictions will bring a huge profit for stock investors. However, conventional time series models produce forecasts based on some strict statistical assumptions about data distributions, and, therefore, they are not very proper to forecast financial datasets. This paper proposes a new forecasting model using adaptive learning techniques to predict TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock index) with multi-stock indexes (NASDAQ stock index and Dow Jones stock index). In verification, this paper employs seven year period of TAIEX stock index, from 1997 to 2003, as experimental datasets, and the root mean square error (RMSE) as evaluation criterion. The performance comparison results show that the proposed model outperforms the listing methods in forecasting Taiwan stock market. Besides, from statistical test results, it is showed that the volatility of Dow Jones and the NASDAQ affect TAIEX significantly.     

Extracting performance rules of suppliers in the manufacturing industry: an empirical study

Performance evaluation of suppliers is increasingly recognized as a critical indicator in supply chain cooperation. Traditional performance evaluation methods have the problems of a simple buy/sell relation and in one’s subjective views between manufacturers and suppliers, and they lack objective automatic evaluation processes in the supply chain considered. Statistical techniques used for evaluation rely on the restrictive assumptions of linear separability, multivariate normality, and independence of the predictive variables. Unfortunately, many of the common models of performance evaluation of suppliers violate these assumptions. The study proposes an integrated model by combining K-means clustering, feature selection, and the decision tree method into a single evaluation model to assess the performance of suppliers and simultaneously tackles the above-mentioned shortcomings. The integrated model is illustrated with an empirical case study of a manufacturer for an original design manufacturer (ODM) to demonstrate the model performance. The experimental results indicate that the proposed method outperforms listed methods in terms of accuracy, and three redundant attributes can be eliminated from the empirical case. Furthermore, the extracted rules by the decision tree C4.5 algorithm form an automatic knowledge system for supplier performance evaluation.     

Evaluating industry performance using extracted RGR rules based on feature selection and rough sets classifier

In strategy of investment, an important thing for investors is to correctly predict firm’s revenue growth rate (RGR), which is an effective evaluation indicator for them to see how big the potential power of future development is and measure how about the growth of future development for a target firm that may be selected to investment portfolios. However, conventional methods of forecasting RGR have some shortcomings such as statistical methods based on strict assumptions of linearity and/or normality limit applications in real world. Additionally, due to rapid changing of information technology (IT) today, some techniques (i.e. rough sets and data mining tools) have become important research trends to both practitioners and academicians. With these reasons above, a new procedure, using the feature selection method and rough sets classifier, is proposed to extract decision rules and improve accuracy rate for classifying RGR. In empirical study, an actual RGR dataset collected from publicly traded company of stock markets is employed to illustrate the proposed procedure. The experimental results of RGR dataset analyses indicate that the proposed procedure surpasses the listing methods in terms of both higher accuracy and fewer attributes, and the output of proposed procedure is to generate a set of easily understandable decision rules that are readily applied in knowledge-based investment systems by investors.     

A new approach for estimating null value in relational database

In general, a database system will not operate properly if it exist some null values of attributes in the system. In this paper, we propose a new approach to estimate null values in relational database, which utilize other clustering algorithm to cluster data, and use fuzzy correlation and distance similarity to calculate the correlation of different attribute. For verifying our method, this paper utilize mean of absolute error rate (MAER) as evaluation criterion to compare with other methods; it is shown that our proposed method proves importance than the existing methods for estimating null values in relational database systems.     

Forecasting the number of outpatient visits using a new fuzzy time series based on weighted-transitional matrix

Forecasting the number of outpatient visits can help the expert of healthcare administration to make a strategic decision. If the number of outpatient visits could be forecast accurately, it would provide the administrators of healthcare with a basis to manage hospitals effectively, to make up a schedule for human resources and finances reasonably, and distribute hospital material resources suitably. This paper proposes a new fuzzy time series method, which is based on weighted-transitional matrix, also proposes two new forecasting methods: the Expectation Method and the Grade-Selection Method. From the verification and results, the proposed methods exhibit a relatively lower error rate in comparison to the listing methods, and could be more stable in facing the ever-changing future trends. The characteristics of the proposed methods could overcome the drawback of the insufficient handling of information to construct a forecasting rule in previous researches.     

Fusion ANFIS models based on multi-stock volatility causality for TAIEX forecasting

Stock market investors value accurate forecasting of future stock price from trading systems because of the potential for large profits. Thus, investors use different forecasting models, such as the time-series model, to assemble a superior investment portfolio. Unfortunately, there are three major drawbacks to the time-series model: (1) most statistical methods rely on some assumptions about the variables; (2) most conventional time-series models use only one variable in forecasting; and (3) the rules mined from artificial neural networks are not easily understandable. To address these shortcomings, this study proposes a new model based on multi-stock volatility causality, a fusion adaptive-network-based fuzzy inference system (ANFIS) procedure, for forecasting stock price problems in Taiwan. Furthermore, to illustrate the proposed model, three practical, collected stock index datasets from the USA and Taiwan stock markets are used in the empirical experiment. The experimental results indicate that the proposed model is superior to the listing methods in terms of root mean squared error, and further evaluation reveals that the profits comparison results for the proposed model produce higher profits than the listing models.     

Evidence-based personal applications of medical computing models in risk factors of cardiovascular disease for the middle-aged and elderly

Medical applications on cardiovascular disease (CVD) for hybrid computing models are an emerging research area. The CVD, including stroke, hypertension, and high cholesterol, is one of 10 leading causes of death in Taiwan in middle-aged and elderly; in particular, the CVD has become the top killer in advanced countries. Thus, this serious but interesting issue triggers the study to focus on patients of the CVD. The study explores variables, influencing cardiovascular functions for four risk factors of blood pressure, blood glucose, blood fat, and kidney diseases, in the middle-aged and elderly. By the data collection of regular physical examination system from a regional hospital, the original dataset contains 52 variables collected from October 2011 to February 2014. We model a hybrid knowledge-based classification system to organize expert experiences, integrated linear and nonlinear attribute selection methods, data discretization of smart expert method, rough set theory, the LEM2 algorithm, and rule-filtering technique to classify the CVD for the early warning purpose. After data cleaning, 20 attributes with 2027 records are remained. For effectively identifying the variables of CVD subjects, this study reclassifies the above four risk diseases into three classes: no disease, 1&2 diseases, and 3&4 diseases. To verify performance of the proposed procedure, we experience an empirical experiment to compare the full 20 used attributes, the used attributes of integrated linear and nonlinear attribute selections with rule-filtering technique, and various classifiers. Conclusively, the 13 used attributes obtained from optimal accuracy become the key determinants that affect the four risk factors of the CVD. The empirical results and findings benefit doctors’ and medical institutions’ early medical recommendations and treatments with the advantages of significantly reducing morbidity of CVD.