Ping-Feng Pai

Software reliability forecasting by support vector machines with simulated annealing algorithms

Support vector machines (SVMs) have been successfully employed to solve non-linear regression and time series problems. However, SVMs have rarely been applied to forecasting software reliability. This investigation elucidates the feasibility of the use of SVMs to forecast software reliability. Simulated annealing algorithms (SA) are used to select the parameters of an SVM model. Numerical examples taken from the existing literature are used to demonstrate the performance of software reliability forecasting. The experimental results reveal that the SVM model with simulated annealing algorithms (SVMSA) results in better predictions than the other methods. Hence, the proposed model is a valid and promising alternative for forecasting software reliability.

Revenue forecasting using a least-squares support vector regression model in a fuzzy environment

Revenue forecasting is difficult but essential for companies that want to create high-quality revenue budgets, especially in an uncertain economic environment with changing government policies. Under these conditions, the subjective judgment of decision makers is a crucial factor in making accurate forecasts. This investigation develops a fuzzy least-squares support vector regression model with genetic algorithms (FLSSVRGA) to forecast seasonal revenues. The FLSSVRGA uses the H-level to control the possibility distribution range yielded by the fuzzy model and to provide the fuzzy prediction interval. Depending on various factors, such as the global economy and government policies, a decision maker can elect a different level for H using the FLSSVRGA. The proposed FLSSVRGA model is a rolling forecasting model with time series data updated monthly that predicts revenue for the coming month. Four other forecasting models: the seasonal autoregressive integrated moving average (SARIMA), generalized regression neural networks (GRNN), support vector regression with genetic algorithms (SVRGA) and least-squares support vector regression with genetic algorithms (LSSVRGA), are employed to forecast the same data sets. The experimental results indicate that the FLSSVRGA model outperforms all four models in terms of forecasting accuracy. Thus, the FLSSVRGA model is a useful alternative for forecasting seasonal time series data in an uncertain environment; it can provide a user-defined fuzzy prediction interval for decision makers.

A support vector machine-based model for detecting top management fraud

Detecting fraudulent financial statements (FFS) is critical in order to protect the global financial market. In recent years, FFS have begun to appear and continue to grow rapidly, which has shocked the confidence of investors and threatened the economics of entire countries. While auditors are the last line of defense to detect FFS, many auditors lack the experience and expertise to deal with the related risks. This study introduces a support vector machine-based fraud warning (SVMFW) model to reduce these risks. The model integrates sequential forward selection (SFS), support vector machine (SVM), and a classification and regression tree (CART). SFS is employed to overcome information overload problems, and the SVM technique is then used to assess the likelihood of FFS. To select the parameters of SVM models, particle swarm optimization (PSO) is applied. Finally, CART is employed to enable auditors to increase substantive testing during their audit procedures by adopting reliable, easy-to-grasp decision rules. The experiment results show that the SVMFW model can reduce unnecessary information, satisfactorily detect FFS, and provide directions for properly allocating audit resources in limited audits. The model is a promising alternative for detecting FFS caused by top management, and it can assist in both taxation and the banking system.

Tourism demand forecasting using novel hybrid system

Accurate prediction of tourism demand is a crucial issue for the tourism and service industry because it can efficiently provide basic information for subsequent tourism planning and policy making. To successfully achieve an accurate prediction of tourism demand, this study develops a novel forecasting system for accurately forecasting tourism demand. The construction of the novel forecasting system combines fuzzy c-means (FCM) with logarithm least-squares support vector regression (LLS-SVR) technologies. Genetic algorithms (GA) were optimally used simultaneously to select the parameters of the LLS-SVR. Data on tourist arrivals to Taiwan and Hong Kong were used. Empirical results indicate that the proposed forecasting system demonstrates a superior performance to other methods in terms of forecasting accuracy.

Time series forecasting by a seasonal support vector regression model

The support vector regression (SVR) model is a novel forecasting approach and has been successfully used to solve time series problems. However, the applications of SVR models in a seasonal time series forecasting has not been widely investigated. This study aims at developing a seasonal support vector regression (SSVR) model to forecast seasonal time series data. Seasonal factors and trends are utilized in the SSVR model to perform forecasts. Furthermore, hybrid genetic algorithms and tabu search (GA/TS) algorithms are applied in order to select three parameters of SSVR models. In this study, two other forecasting models, autoregressive integrated moving average (SARIMA) and SVR are employed for forecasting the same data sets. Empirical results indicate that the SSVR outperforms both SVR and SARIMA models in terms of forecasting accuracy. Thus, the SSVR model is an effective method for seasonal time series forecasting.

Forecasting concentrations of air pollutants by logarithm support vector regression with immune algorithms

Abstract The need to minimize the potential impact of air pollutants on humans has made the accurate prediction of concentrations of air pollutants a crucial subject in environmental research. Support vector regression (SVR) models have been successfully employed to solve time series problems in many fields. The use of SVR models for forecasting concentrations of air pollutants has not been widely investigated. Data preprocessing procedures and the parameter selection of SVR models can radically influence forecasting performance. This study proposes a support vector regression with logarithm preprocessing procedure and immune algorithms (SVRLIA) model which takes advantage of the structural risk minimization of SVR models, the data smoothing of preprocessing procedures, and the optimization of immune algorithms, in order to more accurately forecast concentrations of air pollutants. Three pollutants, namely particulate matter (PM 10 ), nitrogen oxide, (NO x ), and nitrogen dioxide (NO 2 ), are collected and examined to determine the feasibility of the developed SVRLIA model. Experimental results reveal that the SVRLIA model can accurately forecast concentrations of air pollutants.

A fuzzy support vector regression model for business cycle predictions

Business cycle predictions face various sources of uncertainty and imprecision. The uncertainty is usually linguistically determined by the beliefs of decision makers. Thus, the fuzzy set theory is ideally suited to depict vague and uncertain features of business cycle predictions. Consequently, the estimation of fuzzy upper and lower bounds become an essential issue in predicting business cycles in an uncertain environment. The support vector regression (SVR) model is a novel forecasting approach that has been successfully used to solve time series problems. However, the SVR approach has not been widely applied in fuzzy forecasting problems. This study employs support vector regressions to calculate fuzzy upper and lower bounds; and presents a fuzzy support vector regression (FSVR) model for forecasting indices of business cycles. A numerical example of a business cycle prediction in Taiwan was used to demonstrate the forecasting performance of the FSVR model. The empirical results are satisfactory. Therefore, the FSVR model is an effective alternative in forecasting business cycles under uncertain circumstances.

Rough set theory with discriminant analysis in analyzing electricity loads

With the ability to deal with both numeric and nominal information, rough set theory (RST), which can express knowledge in a rule-based form, has been one of the most important techniques in data analysis. However, applications of rough set theory for analyzing electricity loads are not widely discussed. Thus, this investigation employs rough set theory to analyze electricity loads. Additionally, to reduce the time generating reducts by rough set theory, linear discriminant analysis (LDA) is used to generate a reduct for rough set model. Therefore, this study designs a hybrid discriminant analysis and rough set model (DARST) to provide decision rules representing relations in an electric load information system. In this investigation, nine condition factors and variations of electricity loads are employed to examine the feasibility of the hybrid model. Experimental results reveal that the proposed model can efficiently and accurately analyze the relation between condition variables and variations of electricity loads. Consequently, the proposed model is a promising alternative for developing an electric load information system and offers decision rules base for the utility management as well as operations staff.

Forecasting output of integrated circuit industry by support vector regression models with marriage honey-bees optimization algorithms

Integrated circuit (IC) is a vital component of most electronic commodity. IC manufacturing in Taiwan is booming, with revenues from the ICs industry having grown significantly in the recent years. Given the nature of technology, capital intensity and high value-added, accurate forecasting of IC the industry output can improve the competitivity of IC cooperation. Support vector regression (SVR) is an emerging forecasting scheme that has been successfully adopted in many time-series forecasting areas. Additionally, the data preprocessing procedure and the determination of SVR parameters significantly impact the forecasting accuracy of SVR models. Thus, this work develops a support vector regression model with scaling preprocessing and marriage in honey-bee optimization (SVRSMBO) model to accurately forecast IC industry output. The scaling preprocessing procedure is utilized to lower the fluctuation of input data, and the marriage in honey-bees optimization (MBO) algorithm is adopted to determine the three parameters of the SVR model. Numerical data collected from the previous literature are used to demonstrate the performance of the proposed SVRSMBO model. Simulation results indicate that the SVRSMBO model outperforms other forecasting models. Hence, the SVRSMBO model is a promising means of forecasting IC industry output.

Ant colony optimization system for a multi-quantitative and qualitative objective job-shop parallel-machine-scheduling problem

This paper addresses a multi-stage job-shop parallel-machine-scheduling problem with an ant colony optimization system developed. The problem is practically important and yet more complex, especially when customer order splitting in multiple lots for the reduction of operation times in each workstation is allowed. It also includes the decisions of the numbers of parallel machines in workstations dynamically scheduled. In addition, this paper also addresses the multiple-objectives scheduling. For the practical concern, in addition to the production (or quantitative) objectives, the marketing (strategic or qualitative) criteria are also considered. A soft constraint thus may be realized from a thus-called qualitatively evaluated order sequence. The soft constraint with the ant colony optimization solution constructs a penalty function for the multiple qualitative objectives and the results of scheduling obtained by ant colony optimization. For this problem, the ant colony optimization components (including the network representation, tabu lists, transition probabilities, and pheromone trail updating) are also developed and adapted for the multiple objectives. The experiment results of parameter design and different problem sizes are provided. The results of a genetic algorithm also developed for the present problem under the developed system concept are also provided, since in the literature the genetic algorithm has also not been explored for the present problem with multiple objectives and order splitting. The results of both solution techniques show the potential usefulness of the system and are comparable, but the ant colony optimization provides a more computationally efficient better result.