A fuzzy support vector regression model for business cycle predictions

Lin, Kuo-Ping; Pai, Ping-Feng

doi:10.1016/j.eswa.2010.02.071

Cited by 34 publications

(18 citation statements)

References 28 publications

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“…SVM has been successfully applied to solve classification and regression problems in many areas. [10][11][12][13][14][15][16][17][18][19][20] It is called SVR when SVM was employed to solve the regression problems. The basic idea of SVR is to map the input vector x into a high dimensional feature space F using a nonlinear projecting function Φ(x) and then to conduct a liner regression in F space.…”

Section: The Brief Theory Of Support Vector Regressionmentioning

confidence: 99%

Mechanical properties prediction for carbon nanotubes/epoxy composites by using support vector regression

et al. 2015

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Studies have shown there are several process/geometry parameters affecting the mechanical properties of the carbon nanotubes/epoxy composites. The relationship between the response and process/geometry parameters is highly nonlinear and quite complicated. It is very valuable to have an accurate model to estimate the response under different process/geometry parameters. In this paper, the support vector regression (SVR) combined with particle swarm optimization (PSO) for its parameter optimization was employed to construct mathematical models for prediction of mechanical properties of the carbon nanotubes/epoxy composites according to an experimental data set. The leave-one-out cross-validation (LOOCV) test results by SVR models support that the generalization ability of SVR model is high enough. The statistical mean absolute percentage error for tensile strength, elongation and elastic modulus are 3.96%, 3.14% and 2.62%, the correlation coefficients (R 2 ) achieve as high as 0.991, 0.990 and 0.997, respectively. This study suggests that the established SVR model can be used to accurately foresee the mechanical properties of carbon nanotubes/epoxy composites and can be used to optimize designing or controlling of the experimental process/geometry in practice.

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Section: The Brief Theory Of Support Vector Regressionmentioning

confidence: 99%

Mechanical properties prediction for carbon nanotubes/epoxy composites by using support vector regression

et al. 2015

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“…Then the fuzzy prediction model uses (20) and (21) to estimate the estimated central vale Y C (x i ) by a simple calculation as follows:…”

Section: Regressionsmentioning

confidence: 99%

“…For accuracy measuring/forecasting, stochastic/statistics method is usually employed to analysis business cycle. Many researches have devoted to study of business cycle ( [17], [18], [4], [32], [33], [27], and [20]). However, it is difficult to predict the business cycle with time serial, and many researches forecast short-term index of business cycle for reducing complexity.…”

Section: Introductionmentioning

confidence: 99%

Long-term business cycle forecasting using intuitionistic fuzzy least-squares support vector regression

Lin

Hung

2011

2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)

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Long-term business cycle forecasting is a very important issue in economic evaluation. This study presents a novel intuitionistic fuzzy least-squares support vector regression (IFLS-SVR) model for accurately forecasting long-term index of business cycle. Traditional support vector regression (SVR) and least-squares support vector regressions (LS-SVR) have been successfully applied in forecasting problems especially complex/nonlinear system. In this paper, the prediction model adopts two least-squares support vector regressions with intuitionistic fuzzy sets to approach fuzzy upper and lower bounds respectively, and then approach the crisp predict values. Genetic algorithms (GAs) are also employed in order to select two parameters of IFLS-SVR models. In this study, IFLS-SVR, LS-SVR and SVR are employed for the Taiwan business index forecasting. Empirical results indicate that the IFLS-SVR has better performance in terms of forecasting accuracy. Therefore, the IFLS-SVR model can efficiently provide credible long-term prediction for the Taiwan business index forecastingKeywords-long-term business cycle forecasting; intuitionistic fuzzy support vector regression; support vector regression; least-squares support vector regressions

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“…In their application, they confirm the forecasting superiority of their proposed technique compared to chaos-based NNs and several traditional non-linear models. Lin and Pai (2010) introduce a fuzzy SVR model for forecasting indices of business cycles, Kim and Sohn (2010) forecast the credit score of small and medium enterprises with SVM, while Wu and Akbarov (2011) apply successfully weighted SVRs to the task of forecasting warranty claims. Moreover, Jiang and He (2012) propose a hybrid SVR that incorporates the Grey relational grade weighting function.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms—Support vector regression forecast combinations

Sermpinis

Theofilatos

Karathanasopoulos

2015

European Journal of Operational Research

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Modeling, Forecasting and Trading the EUR Exchange Rates with Hybrid Rolling Genetic Algorithms -Support Vector Regression Forecast CombinationsGeorgios Sermpinis* Charalampos Stasinakis** Konstantinos Theofilatos*** Andreas Karathanasopoulos**** June 2015 AbstractThe motivation of this paper is to introduce a hybrid Rolling Genetic Algorithm -Support Vector Regression (RG-SVR) model for optimal parameter selection and feature subset combination. The algorithm is applied to the task of forecasting and trading the EUR/USD, EUR/GBP and EUR/JPY exchange rates. The proposed methodology genetically searches over a feature space (pool of individual forecasts) and then combines the optimal feature subsets (SVR forecast combinations) for each exchange rate. This is achieved by applying a fitness function specialized for financial purposes and adopting a sliding window approach. The individual forecasts are derived from several linear and non-linear models. RG-SVR is benchmarked against genetically and non-genetically optimized SVRs and SVMs models that are dominating the relevant literature, along with the robust ARBF-PSO neural network. The statistical and trading performance of all models is investigated during the period of 1999-2012. As it turns out, RG-SVR presents the best performance in terms of statistical accuracy and trading efficiency for all the exchange rates under study. This superiority confirms the success of the implemented fitness function and training procedure, while it validates the benefits of the proposed algorithm.

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A fuzzy support vector regression model for business cycle predictions

Cited by 34 publications

References 28 publications

Mechanical properties prediction for carbon nanotubes/epoxy composites by using support vector regression

Mechanical properties prediction for carbon nanotubes/epoxy composites by using support vector regression

Long-term business cycle forecasting using intuitionistic fuzzy least-squares support vector regression

Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms—Support vector regression forecast combinations

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