دانلود مقاله ISI انگلیسی شماره 25263
عنوان فارسی مقاله

مدل رگرسیون بردار پشتیبانی فازی برای پیش بینی چرخه کسب و کار

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
25263 2010 6 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
A fuzzy support vector regression model for business cycle predictions
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Expert Systems with Applications, Volume 37, Issue 7, July 2010, Pages 5430–5435

کلمات کلیدی
چرخه کسب و کار - نظریه مجموعه فازی - رگرسیون بردار پشتیبانی
پیش نمایش مقاله
پیش نمایش مقاله مدل رگرسیون بردار پشتیبانی فازی برای پیش بینی چرخه کسب و کار

چکیده انگلیسی

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.

مقدمه انگلیسی

Accuracy in forecasting business cycles is an important issue in economic study, and statistical methods have usually been employed to analyze them. Many investigations have been done in the analysis of business cycles (Banerji and Hiris, 2001, Layton, 1996, Layton, 1998, Seip and McNown, 2007, Wu and Tseng, 2002 and Yang and Kim, 2005). However, business cycles are often determined by a panel of macroeconomic experts, and thus, it is difficult to predict the index of business cycles. The difficulty arises from assumptions made from the probability distributions and business cycle data, which are usually vague. The index of business cycles in Taiwan is composed of nine exogenous variables, and five lights are used to represent different economic activities. The five lights include some uncertain factors in predicting business cycles. Hence, the fuzzy set theory (Zadeh, 1965) is a proper approach to analyze Taiwan business cycles. Unlike most of traditional technologies SVR (Vapnik, Golowich, & Smola, 1996) implementing neural network models, SVR adopts a structural risk minimization principle, which seeks to minimize the upper bounds of the generalization error rather than minimize the training error. In recent years, SVR schemes have been extended to cope with forecasting problems, and have provided many promising results in customer demand (Levis & Papageorgiou, 2005), finance (Huang et al., 2005, Kim, 2003 and Tay and Cao, 2002), intermittent demand (Hua & Zhang, 2006), tourism demand (Pai & Hong, 2005), air quality (Lu & Wang, 2005), wind speed (Mohandes, Halawani, Rehman, & Hussain, 2004), plant control systems (Xi, Poo, & Chou, 2007), rainfall (Hong & Pai, 2007), prices for the electricity market (Gaoa, Bompard, Napoli, & Cheng, 2007), and flood control (Yu, Chen, & Chang, 2006). Hong and Hwang (2003) proposed a support vector fuzzy regression machine model for modifying convex optimization problems of multivariate fuzzy linear regression models. Empirical results indicate that the developed model derives satisfying solutions efficiently. Jeng, Chuang, and Su (2003) developed a support vector interval regression network to efficiently handle interval output data. Yao and Yu (2006) developed a fuzzy regression based on asymmetric support vector machines, which overcome limitations of traditional nonlinear fuzzy regression, and can be effectively used for parameter estimation. Chuang (2008) presented an interval support vector regression network model, which can handle interval input and output data. Hao and Chiang (2008) developed a fuzzy regression analysis model based on support vector learning techniques, and suggested that the developed model can perform automatic and accurate control in fuzzy regression analysis tasks. In this study, a fuzzy support vector regression model is presented to forecast an index of business cycles. Support vector regression was used to calculate fuzzy upper and lower bounds, and then make predictions by fuzzy H-level set (H-cut). In addition, genetic algorithms (GA) were employed to select three parameters of SVR models. The remainder of this paper is organized as follows. A brief introduction of the theory of SVR is given in Section 2. The fuzzy support vector regression model is derived in Section 3. A numerical example of business cycle predictions and empirical results are presented in Section 4. Some concluding remarks are offered in Section 5.

نتیجه گیری انگلیسی

Due to the recent global economic recession, analysis of business cycles is increasingly crucial. This study develops a FSVR model to exploit the unique strength of the fuzzy set theory and the SVR technique, in order to predict business cycles in Taiwan. Simulation results indicate that the FSVR model offers a promising alternative in business cycles in uncertain circumstances. The superior performance of the FSVR model can be ascribed to two causes. First, the SVR can efficiently capture trends of nonlinear data, and precisely estimate upper bounds and lower bounds of fuzzy numbers. Second, based on sensitive analysis of H-level, the FSVR model can provide creditable predictions for Taiwanese business cycle predictions. For future work, forecasting other types of uncertain time series data by the FSVR model is a challenging issue for study. Future studies can also consider using data preprocessing techniques to improve the forecasting accuracy of the FSVR model.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.