Robustness Analysis of Novel ε-Support Vector Regression

In case of experimental data contaminated with errors and noise, the robust ε-support vector regression has good forecast accuracy and high generalization ability. However, it depends on the selection of system parameter. Firstly, this paper introduces the robust ε-support vector regression method. Secondly, as the experiments prove, the new method achieves high forecast accuracy by virtue of the optimal penalty parameter C. Finally, the optimal method of parameter C is presented in the last section.

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Comparative Analysis of Statistical, Machine Learning, and Grey Methods for Short-Term Electricity Load Forecasting

Nature-Inspired Computing ◽

10.4018/978-1-5225-0788-8.ch044 ◽

2016 ◽

pp. 1161-1183 ◽

Cited By ~ 1

Author(s):

Tuncay Ozcan ◽

Tarik Küçükdeniz ◽

Funda Hatice Sezgin

Keyword(s):

Support Vector Regression ◽

Electricity Market ◽

Load Forecasting ◽

Forecast Accuracy ◽

Grey Model ◽

Support Vector ◽

Forecasting Model ◽

Data Sets ◽

Electricity Load ◽

Electricity Load Forecasting

Electricity load forecasting is crucial for electricity generation companies, distributors and other electricity market participants. In this study, several forecasting techniques are applied to time series modeling and forecasting of the hourly loads. Seasonal grey model, support vector regression, random forests, seasonal ARIMA and linear regression are benchmarked on seven data sets. A rolling forecasting model is developed and 24 hours of the next day is predicted for the last 14 days of each data set. This day-ahead forecasting model is especially important in day-ahead market activities and plant scheduling operations. Experimental results indicate that support vector regression and seasonal grey model outperforms other approaches in terms of forecast accuracy for day-ahead load forecasting.

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A Method of Improved Support Vector Machine for Network Security Situation Forecasting

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.187.291 ◽

2011 ◽

Vol 187 ◽

pp. 291-296

Author(s):

Yuan Cheng Li ◽

Jing Tao Jing

Keyword(s):

Network Security ◽

Forecast Accuracy ◽

Forecast Model ◽

Selection Method ◽

Support Vector ◽

Swarm Optimization ◽

Parameters Selection ◽

Vector Machines ◽

Good Forecast ◽

Improved Support Vector Machine

Aiming at the problem that parameters of Support Vector Machines (SVM) are very difficult to confirm, this paper points out a parameter selection method for SVM based on Particle Swarm Optimization (PSO), which can make the SVM more scientific and reasonable in parameters selection; and thus enhance the forecast accuracy of the network security situation. The Simulation results show that the optimized SVR forecast model has good forecast accuracy for the network security situation, and present the future changing at a macro level, then help the network managers control network.

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A New Kernel of Support Vector Regression for Forecasting High-Frequency Stock Returns

Mathematical Problems in Engineering ◽

10.1155/2016/4907654 ◽

2016 ◽

Vol 2016 ◽

pp. 1-9 ◽

Cited By ~ 5

Author(s):

Hui Qu ◽

Yu Zhang

Keyword(s):

Support Vector Regression ◽

Stock Returns ◽

High Frequency ◽

Forecast Accuracy ◽

Support Vector ◽

Nonlinear Relationship ◽

Sigmoid Function ◽

Accuracy Rate ◽

Out Of Sample ◽

Future Return

This paper investigates the value of designing a new kernel of support vector regression for the application of forecasting high-frequency stock returns. Under the assumption that each return is an event that triggers momentum and reversal periodically, we decompose each future return into a collection of decaying cosine waves that are functions of past returns. Under realistic assumptions, we reach an analytical expression of the nonlinear relationship between past and future returns and introduce a new kernel for forecasting future returns accordingly. Using high-frequency prices of Chinese CSI 300 index from January 4, 2010, to March 3, 2014, as empirical data, we have the following observations: (1) the new kernel significantly beats the radial basis function kernel and the sigmoid function kernel out-of-sample in both the prediction mean square error and the directional forecast accuracy rate. (2) Besides, the capital gain of a simple trading strategy based on the out-of-sample predictions with the new kernel is also significantly higher. Therefore, we conclude that it is statistically and economically valuable to design a new kernel of support vector regression for forecasting high-frequency stock returns.

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Particle Swarm Optimization-Based Support Vector Regression for Tourist Arrivals Forecasting

Computational Intelligence and Neuroscience ◽

10.1155/2018/6076475 ◽

2018 ◽

Vol 2018 ◽

pp. 1-13 ◽

Cited By ~ 8

Author(s):

Hsiou-Hsiang Liu ◽

Lung-Cheng Chang ◽

Chien-Wei Li ◽

Cheng-Hong Yang

Keyword(s):

Particle Swarm Optimization ◽

Support Vector Regression ◽

Particle Swarm ◽

Forecast Accuracy ◽

Tourism Industry ◽

Support Vector ◽

Tourism Demand ◽

Economic Sectors ◽

Data Set ◽

Swarm Optimization

The tourism industry has become one of the most important economic sectors for governments worldwide. Accurately forecasting tourism demand is crucial because it provides useful information to related industries and governments, enabling stakeholders to adjust plans and policies. To develop a forecasting tool for the tourism industry, this study proposes a method that combines feature selection (FS) and support vector regression (SVR) with particle swarm optimization (PSO), named FS–PSOSVR. To ensure high forecast accuracy, FS and a PSO algorithm are employed to, respectively, select reliable input variables and to identify the optimal initial parameters of SVR. The proposed method was tested using a data set of monthly tourist arrivals to Taiwan from January 2006 to December 2016. The results reveal that the errors obtained using FS–PSOSVR are comparatively smaller than those obtained using other methods, indicating that FS–PSOSVR is an effective method for forecasting tourism demand.

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Forecasting Time Series Movement Direction with Hybrid Methodology

Journal of Probability and Statistics ◽

10.1155/2017/3174305 ◽

2017 ◽

Vol 2017 ◽

pp. 1-8 ◽

Cited By ~ 3

Author(s):

Salwa Waeto ◽

Khanchit Chuarkham ◽

Arthit Intarasit

Keyword(s):

Time Series ◽

Support Vector Regression ◽

Hybrid Model ◽

Moving Average ◽

Forecast Accuracy ◽

Movement Direction ◽

Support Vector ◽

Autoregressive Integrated Moving Average ◽

Proposed Model ◽

Hybrid Methodology

Forecasting the tendencies of time series is a challenging task which gives better understanding. The purpose of this paper is to present the hybrid model of support vector regression associated with Autoregressive Integrated Moving Average which is formulated by hybrid methodology. The proposed model is more convenient for practical usage. The tendencies modeling of time series for Thailand’s south insurgency is of interest in this research article. The empirical results using the time series of monthly number of deaths, injuries, and incidents for Thailand’s south insurgency indicate that the proposed hybrid model is an effective way to construct an estimated hybrid model which is better than the classical time series model or support vector regression. The best forecast accuracy is performed by using mean square error.

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Comparative Analysis of Statistical, Machine Learning, and Grey Methods for Short-Term Electricity Load Forecasting

Intelligent Techniques for Data Analysis in Diverse Settings - Advances in Data Mining and Database Management ◽

10.4018/978-1-5225-0075-9.ch005 ◽

2016 ◽

pp. 96-118

Author(s):

Tuncay Ozcan ◽

Tarik Küçükdeniz ◽

Funda Hatice Sezgin

Keyword(s):

Support Vector Regression ◽

Electricity Market ◽

Load Forecasting ◽

Forecast Accuracy ◽

Grey Model ◽

Support Vector ◽

Forecasting Model ◽

Data Sets ◽

Electricity Load ◽

Electricity Load Forecasting

Electricity load forecasting is crucial for electricity generation companies, distributors and other electricity market participants. In this study, several forecasting techniques are applied to time series modeling and forecasting of the hourly loads. Seasonal grey model, support vector regression, random forests, seasonal ARIMA and linear regression are benchmarked on seven data sets. A rolling forecasting model is developed and 24 hours of the next day is predicted for the last 14 days of each data set. This day-ahead forecasting model is especially important in day-ahead market activities and plant scheduling operations. Experimental results indicate that support vector regression and seasonal grey model outperforms other approaches in terms of forecast accuracy for day-ahead load forecasting.

Download Full-text