scholarly journals A least square support vector machine prediction algorithm for chaotic time series based on the iterative error correction

2014 ◽  
Vol 63 (5) ◽  
pp. 050505
Author(s):  
Tang Zhou-Jin ◽  
Ren Feng ◽  
Peng Tao ◽  
Wang Wen-Bo
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Error Correction ◽  
Chaotic Time Series ◽  
Least Square ◽  
Prediction Algorithm ◽  
Support Vector ◽  
Iterative Error

Study on Prediction of Chaotic Time Series Using Least Squares Support Vector Machines

2014 ◽  
Vol 1061-1062 ◽  
pp. 935-938
Author(s):  
Xin You Wang ◽  
Guo Fei Gao ◽  
Zhan Qu ◽  
Hai Feng Pu
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Least Squares ◽  
Prediction Accuracy ◽  
Chaotic Time Series ◽  
Support Vector ◽  
Vector Machines ◽  
Online Prediction ◽  
Better Than

The predictions of chaotic time series by applying the least squares support vector machine (LS-SVM), with comparison with the traditional-SVM and-SVM, were specified. The results show that, compared with the traditional SVM, the prediction accuracy of LS-SVM is better than the traditional SVM and more suitable for time series online prediction.

Traffic Speed Time Series Short Term Forecasting Using Aggregated Model

2014 ◽  
Vol 587-589 ◽  
pp. 2057-2062
Author(s):  
Jian Gu ◽  
Shu Yan Chen
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Least Square ◽  
Support Vector ◽  
Short Term ◽  
Support Vector Machine Regression ◽  
Prediction Ability ◽  
Aggregated Model ◽  
Traffic Speed ◽  
Short Term Forecasting

This paper integrated superiority from time series model and least square support vector machine regression model with data aggregation for traffic speed short term forecasting. Based on the results of traffic data variations analysis, the practicability that speed data can be aggregated to several periods was confirmed, and aggregated model can be developed to forecast the speed with auto regression (AR) model and support vector machine regression (SVR). Then the speed data in case study were integrated to 4 periods at the location of Remote Traffic Microwave Sensors (RTMS) 2047 on 2ndRing Road Expressway in Beijing. Arguments with coefficients from AR models then act as the independent variables of LSSVR in aggregated model. Short term traffic speed was predicted by aggregated model, and the results indicated that taking advantages of time periods variation rule inside the aggregated model would help save the model running time cost under the premise of accuracy with better prediction ability than LSSVR in certain conditions.

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