Support Vector Regression Prediction Method of Text Data Based on Correlation Analysis

2021 ◽  
pp. 595-601
Author(s):  
Jianfeng Ma ◽  
Tongfei Shang ◽  
Jingwei Yang ◽  
Jiaqing Zhao
Keyword(s):  
Correlation Analysis ◽  
Support Vector Regression ◽  
Prediction Method ◽  
Support Vector ◽  
Text Data

scholarly journals Copper Price Prediction Using Support Vector Regression Technique

2020 ◽  
Vol 10 (19) ◽  
pp. 6648
Author(s):  
Gabriel Astudillo ◽  
Raúl Carrasco ◽  
Christian Fernández-Campusano ◽  
Máx Chacón
Keyword(s):  
Support Vector Regression ◽  
Mining Industry ◽  
Prediction Method ◽  
Real Data ◽  
Copper Price ◽  
Support Vector ◽  
Metal Exchange ◽  
Potential Growth ◽  
Support Vector Regression Model ◽  
Price Prediction

Predicting copper price is essential for making decisions that can affect companies and governments dependent on the copper mining industry. Copper prices follow a time series that is nonlinear and non-stationary, and that has periods that change as a result of potential growth, cyclical fluctuation and errors. Sometimes, the trend and cyclical components together are referred to as a trend-cycle. In order to make predictions, it is necessary to consider the different characteristics of a trend-cycle. In this paper, we study a copper price prediction method using support vector regression (SVR). This work explores the potential of the SVR with external recurrences to make predictions at 5, 10, 15, 20 and 30 days into the future in the copper closing price at the London Metal Exchange. The best model for each forecast interval is performed using a grid search and balanced cross-validation. In experiments on real data sets, our results obtained indicate that the parameters (C, ε, γ) of the model support vector regression do not differ between the different prediction intervals. Additionally, the amount of preceding values used to make the estimates does not vary according to the predicted interval. Results show that the support vector regression model has a lower prediction error and is more robust. Our results show that the presented model is able to predict copper price volatilities near reality, as the root-mean-square error (RMSE) was equal to or less than the 2.2% for prediction periods of 5 and 10 days.

Damage degree prediction method of CFRP structure based on fiber Bragg grating and epsilon-support vector regression

Optik ◽  
2019 ◽  
Vol 180 ◽  
pp. 244-253 ◽  
Author(s):  
Shizeng Lu ◽  
Mingshun Jiang ◽  
Xiaohong Wang ◽  
Hongliang Yu ◽  
Chenhui Su
Keyword(s):  
Support Vector Regression ◽  
Fiber Bragg Grating ◽  
Prediction Method ◽  
Support Vector ◽  
Bragg Grating ◽  
Damage Degree

scholarly journals Life prediction of slewing bearing based on isometric mapping and fuzzy support vector regression

2019 ◽  
Vol 42 (1) ◽  
pp. 94-103 ◽  
Author(s):  
Weigang Bao ◽  
Hua Wang ◽  
Jie Chen ◽  
Bo Zhang ◽  
Peng Ding ◽  
...  
Keyword(s):  
Prediction Model ◽  
Support Vector Regression ◽  
Life Prediction ◽  
Feature Fusion ◽  
Prediction Method ◽  
Support Vector ◽  
Isometric Mapping ◽  
Slewing Bearing ◽  
Time Frequency ◽  
Life Prediction Model

The monitoring data of slewing bearing is massive. In order to establish accurate life prediction model from complex vibration signal of slewing bearing, a life prediction method based on manifold learning and fuzzy support vector regression (SVR) is proposed. Firstly, the multiple features are extracted from time domain and time-frequency domain. Then isometric mapping (ISOMAP) is used to reduce high-dimensional features to low-dimensional features that can reflect degeneration of slewing bearing well. Finally, the fuzzy SVR is used to predict the life degradation trend of slewing bearing. The results show that: (1) Multi-feature fusion after ISOMAP can obtain more comprehensive degradation indicator. (2) The complexity of the life prediction model is simplified and the real-time life degradation trend of slewing bearing can be well predicted by fuzzy SVR, so it is very suitable to predict life degradation trend of slewing bearing based on massive data well. The time of prediction on average is reduced by 72.7%. The mean absolute error (MAE) and root mean square error (RMSE) of prediction are reduced by 73% and 59% respectively compared with traditional methods. The accuracy of prediction is greatly improved.

scholarly journals Dynamic Heat Supply Prediction Using Support Vector Regression Optimized by Particle Swarm Optimization Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Meiping Wang ◽  
Qi Tian
Keyword(s):  
Particle Swarm Optimization ◽  
Correlation Analysis ◽  
Support Vector Regression ◽  
Influencing Factors ◽  
Heat Supply ◽  
Particle Swarm ◽  
Heating System ◽  
Support Vector ◽  
Swarm Optimization ◽  
Input Variables

We developed an effective intelligent model to predict the dynamic heat supply of heat source. A hybrid forecasting method was proposed based on support vector regression (SVR) model-optimized particle swarm optimization (PSO) algorithms. Due to the interaction of meteorological conditions and the heating parameters of heating system, it is extremely difficult to forecast dynamic heat supply. Firstly, the correlations among heat supply and related influencing factors in the heating system were analyzed through the correlation analysis of statistical theory. Then, the SVR model was employed to forecast dynamic heat supply. In the model, the input variables were selected based on the correlation analysis and three crucial parameters, including the penalties factor, gamma of the kernel RBF, and insensitive loss function, were optimized by PSO algorithms. The optimized SVR model was compared with the basic SVR, optimized genetic algorithm-SVR (GA-SVR), and artificial neural network (ANN) through six groups of experiment data from two heat sources. The results of the correlation coefficient analysis revealed the relationship between the influencing factors and the forecasted heat supply and determined the input variables. The performance of the PSO-SVR model is superior to those of the other three models. The PSO-SVR method is statistically robust and can be applied to practical heating system.

scholarly journals Application of Data Mining Technology Based on Wireless Sensor Networks in Oceanographic Forecasting

2018 ◽  
Vol 14 (04) ◽  
pp. 137 ◽  
Author(s):  
Wei Zhai
Keyword(s):  
Support Vector Regression ◽  
Prediction Method ◽  
Advanced Technology ◽  
Wireless Sensor ◽  
Utilization Rate ◽  
Observation System ◽  
Support Vector ◽  
Good Prediction ◽  
Hydrological Data ◽  
Good Prediction Accuracy

This paper aims to present a desirable prediction method for oceanographic trends. Therefore, an online monitoring scheme was prepared to collect the accurate oceanographic hydrological data based on wireless sensor network (WSN) and computer technology. Then, the data collected by the WSN were processed by support vector regression algorithm. To obtain the most important parameters of the algorithm, the particle swarm optimization was introduced to search for the global optimal solution through the coopetition between the particles. After that, an oceanographic hydrological data collection and observation system was created based on the hydrological situation of New York harbour. Then, the traditional support vector regression and the proposed method were applied to predict the oceanographic trends based on water temperature, salinity and other indices. The results show that the proposed algorithm enhanced the data utilization rate of the WSN, and achieved good prediction accuracy. The research provides important insights into the application of advanced technology in oceanographic forecast.

An Alternative Approach to Prediction of Critical Heat Flux: Projection Support Vector Regression

2014 ◽  
Author(s):  
Botao Jiang ◽  
Fuyu Zhao
Keyword(s):  
Heat Flux ◽  
Support Vector Regression ◽  
Critical Heat Flux ◽  
Cooling System ◽  
Prediction Method ◽  
Training Data ◽  
Support Vector ◽  
Training Set ◽  
Test Set ◽  
Alternative Approach

Critical heat flux (CHF) is one of the most crucial design criteria in other boiling systems such as evaporator, steam generators, fuel cooling system, boiler, etc. This paper presents an alternative CHF prediction method named projection support vector regression (PSVR), which is a combination of feature vector selection (FVS) method and support vector regression (SVR). In PSVR, the FVS method is first used to select a relevant subset (feature vectors, FVs) from the training data, and then both the training data and the test data are projected into the subspace constructed by FVs, and finally SVR is applied to estimate the projected data. An available CHF dataset taken from the literature is used in this paper. The CHF data are split into two subsets, the training set and the test set. The training set is used to train the PSVR model and the test set is then used to evaluate the trained model. The predicted results of PSVR are compared with those of artificial neural networks (ANNs). The parametric trends of CHF are also investigated using the PSVR model. It is found that the results of the proposed method not only fit the general understanding, but also agree well with the experimental data. Thus, PSVR can be used successfully for prediction of CHF in contrast to ANNs.

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