Fuzzy Support Vector Regression Model Research Articles

Application of fuzzy support vector regression machine in power load prediction

Power system load forecasting is a method that uses historical load data to predict electricity load data for a future time period. Aiming at the problems of general prediction accuracy and slow prediction speed in using typical machine learning methods, an improved fuzzy support vector regression machine method is proposed for power load forecasting. In this method, the boundary vector extraction technique is employed in the design of the membership function for fuzzy support vectors to differentiate the importance of different samples in the regression process. This method utilizes a membership function based on boundary vectors to assign differential weights to different sample points that used to differentiate the importance of different types of samples in the regression analysis process in order to improve the accuracy of electricity load prediction. The key parameters of the fuzzy support vector regression model are optimized, further enhancing the precision of the forecasting results. Simulation experiments are conducted using real power load data sets, and the experimental results demonstrate the effectiveness of the proposed method in terms of accuracy and speed in predicting power load data compared to other prediction models. This method can be widely applied in real power production and scheduling processes.

Analysis and Forecasting of International Airport Traffic Volume

Globalization has resulted in increases in air transportation demand and air passenger traffic. With the increases in air traffic, airports face challenges related to infrastructure, air services, and future development. Air traffic forecasting is essential to ensuring appropriate investment in airports. In this study, we combined fuzzy theory with support vector regression (SVR) to develop a fuzzy SVR (FSVR) model for forecasting international airport traffic. This model was used to predict the air traffic volumes at the world’s 10 busiest airports in terms of air traffic in 2018. The predictions were made for the period from August 2014 to December 2019. For fuzzy time series, the developed FSVR model can consider historical air traffic changes. The FSVR model can suitably divide air traffic changes into appropriate fuzzy sets, generate membership function values, and establish fuzzy relations to produce fuzzy interpolated values with minimal errors. Thus, in the prediction of continuous data, the fuzzy data with the smallest errors can be subjected to SVR to find the optimal hyperplane model with the minimum distance to the appropriate support vector sample points. The performance of the proposed model was compared with those of five other models. Of the compared models, the FSVR model exhibited the lowest mean absolute percentage error (MAPE), mean absolute error, and root mean square error for all types of traffic at all of the airports analyzed; all of the MAPE values were below 2.5. The FSVR model can predict future growth trends in air traffic, air passenger flows, aircraft flows, and logistics. An airport authority can use this model to analyze the existing operational facilities and service capacity, find bottlenecks in airport operations, and create a blueprint for future development. The findings revealed that implementing a hybrid modeling approach, specifically the FSVR model, can significantly enhance the performance of the SVR model. The FSVR model allows airlines to predict traffic growth patterns, identify viable new destinations, optimize their schedules or fleet, make accurate marketing decisions, and plan traffic effectively. The FSVR model can guide the timely construction of appropriate airport facilities with accurate predictions. Rapid, cost-effective, efficient, and balanced transportation planning enables the provision of fast, cost-effective, comfortable, safe, and convenient passenger and cargo services while ensuring the proper planning of the airport’s capacity for land-side transportation connections.

Improved SVR machine learning models for agricultural drought prediction at downstream of Langat River Basin, Malaysia

Abstract Drought is a harmful and little understood natural hazard. Effective drought prediction is vital for sustainable agricultural activities and water resources management. The support vector regression (SVR) model and two of its enhanced variants, namely, fuzzy-support vector regression (F-SVR) and boosted-support vector regression (BS-SVR) models, for predicting the standardized precipitation evapotranspiration indices (SPEI) (in this case, SPEI-1, SPEI-3 and SPEI-6, at various timescales) with a lead time of one month, were developed to minimize potential drought impact on oil palm plantations at the downstream end of the Langat River Basin, which has a tropical climate pattern. Observed SPEIs from periods 1976 to 2011 and 2012 to 2015 were used for model training and validation, respectively. By applying the MAE, RMSE, MBE and R2 as model assessments, it was found that the F-SVR model was best with the trend of improving accuracy when the timescale of the SPEIs increased. It was also found that differences in model performance deteriorates with increased timescale of the SPEIs. The outlier reducing effect from the fuzzy concept has better improvement for the SVR-based models compared to the boosting technique in predicting SPEI-1, SPEI-3 and SPEI-6 for a one-month lead time at the downstream of Langat River Basin.

Fuzzy support vector regression model for forecasting stock market volatility

Stock market volatility exhibits characteristics such as clustering and time-varying fluctuations. This paper proposes a two-stage method for addressing these concerns. The involved procedure is as follows: First, a fuzzy system is used to analyze clustering regimes according to the size of fluctua tions. Second, the clustering regimes of Stage I are used to establish a support vector regression (SVR) model, which is used to reduce the time-varying complexity. However, the fuzzy-SVR model combines the parameters of membership functions and SVR models, further complicating the problem. Thus, this paper presents parallel research based on a genetic algorithm (GA) for estimating the parameters of the membership functions and SVR model. Data from four stock markets—the Taiwan Stock Exchange weighted stock index (Taiwan), the NASDAQ Composite index, the Hang Seng index (Hong Kong), and the Shanghai Composite index (Shanghai)—were analyzed in this study to illustrate the performance of the proposed model. According to the simulation results, the forecasting of out-of-sample volatility performance was significantly improved when the model accounted for the behavioral effect of both clustering and time-varying fluctuations.

An integrated fuzzy support vector regression and the particle swarm optimization algorithm to predict indoor thermal comfort

The thermal comfort indices are usually identified using empirical thermal models based on the human balanced equations and experimentations. In our paper, we propose a statistical regression method to predict these indices. To achieve this goal, first, the fuzzy support vector regression (FSVR) identification approach was integrated with the particle swarm optimization (PSO) algorithm. Then PSO was used as a global optimizer to optimize and select the hyper-parameters needed for the FSVR model. The radial basis function (RBF) kernel was used within the FSVR model. Afterward, these optimal hyper-parameters were used to forecast the thermal comfort indices: predicted mean vote (PMV), predicted percentage dissatisfied (PPD), new standard effective temperature (SET*), thermal discomfort (DISC), thermal sensation (TSENS) and predicted percent dissatisfied due to draft (PD). The application of the proposed approach on different data sets gave successful prediction and promising results. Moreover, the comparisons between the traditional Fanger model and the new model further demonstrate that the proposed model achieves even better identification performance than the original FSVR technique.

Evolutionary fuzzy SVR modeling of weld residual stress

Residual stresses are an integral part of the total stress acting on any component in service. It is important to determine and/or predict the magnitude, nature and direction of the residual stress to estimate the life of important engineering parts, particularly welded components. Researchers have developed many direct measuring techniques for welding residual stress. Intelligent techniques have been developed to predict residual stresses to meet the demands of advanced manufacturing planning. This research paper explores the development of Finite Element model and evolutionary fuzzy support vector regression model for the prediction of residual stress in welding. Residual stress model is developed using Finite Element Simulation. Results from Finite Element Method (FEM) model are used to train and test the developed Fuzzy Support Vector Regression model tuned with Genetic Algorithm (FSVRGA) using K-fold cross validation method. The performance of the developed model is compared with Support Vector Regression model and Fuzzy Support Vector Regression model. The proposed and developed model is superior in terms of computational speed and accuracy. Developed models are validated and reported. The developed model finds scope in setting the initial weld process parameters.

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.