scholarly journals Short-Term Financial Time Series Forecasting Integrating Principal Component Analysis and Independent Component Analysis with Support Vector Regression

2018 ◽  
Vol 06 (03) ◽  
pp. 51-67 ◽  
Author(s):  
Utpala Nanda Chowdhury ◽  
Sanjoy Kumar Chakravarty ◽  
Md. Tanvir Hossain
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Independent Component Analysis ◽  
Support Vector Regression ◽  
Financial Time Series ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Short Term ◽  
Financial Time

scholarly journals Assimilation of Principal Component Analysis and Wavelet with Kernel Support Vector Regression for Medium-Term Financial Time Series Forecasting

2020 ◽  
Vol 4 (7) ◽  
pp. 40-48
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Support Vector Regression ◽  
Financial Time Series ◽  
Principal Component ◽  
Component Analysis ◽  
Time Series Forecasting ◽  
Series Data ◽  
Support Vector ◽  
Financial Time

Entities and institutional financiers have gained a lot of growth from financial time series forecasting in recent times. But the major challenges of financial time series data are the high noise and complexity of its nature. Researchers in recent times have successfully engaged the application of support vector regression (SVR) to conquer this challenge. In this study principal component analysis (PCA) is applied to extract the low dimensionality and efficient feature information, while wavelet is used to pre-process the extracted features in other to nu1llify the influence of the noise in the features with a KSVR based forecasting model. The analysis is carried out based on the quarterly tax revenue data of 39 years from the first quarter of 1981 to the last quarter of 2016. The forecasting is made for ten quarters ahead. The initial empirical result shows that the multicollinearity has been reduced to zero (0), and the analytic result reveals that the proposed model PCA-W-KSVR outperforms KSVR, PCA-KSVR, and W-KSVR in terms of MAE, MAPE, MSE and RMSE

scholarly journals Financial time series forecasting using twin support vector regression

PLoS ONE ◽  
2019 ◽  
Vol 14 (3) ◽  
pp. e0211402 ◽  
Author(s):  
Deepak Gupta ◽  
Mahardhika Pratama ◽  
Zhenyuan Ma ◽  
Jun Li ◽  
Mukesh Prasad
Keyword(s):  
Time Series ◽  
Support Vector Regression ◽  
Financial Time Series ◽  
Time Series Forecasting ◽  
Support Vector ◽  
Twin Support Vector Regression ◽  
Financial Time

Support Vector Regression for Missing Data Estimation

2010 ◽  
pp. 117-141
Author(s):  
Tshilidzi Marwala
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Regression ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Overall Performance ◽  
Accuracy Measures ◽  
Missing Variables ◽  
Missing Data Estimation ◽  
Data Estimation

This chapter develops and compares the merits of three different data imputation models by using accuracy measures. The three methods are auto-associative neural networks, a principal component analysis and support vector regression all combined with cultural genetic algorithms to impute missing variables. The use of a principal component analysis improves the overall performance of the auto-associative network while the use of support vector regression shows promising potential for future investigation. Imputation accuracies up to 97.4% for some of the variables are achieved.

scholarly journals Near-field sound source localization using principal component analysis–multi-output support vector regression

2020 ◽  
Vol 16 (4) ◽  
pp. 155014772091640
Author(s):  
Lanmei Wang ◽  
Yao Wang ◽  
Guibao Wang ◽  
Jianke Jia
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Regression ◽  
Covariance Matrix ◽  
Signal To Noise Ratio ◽  
Near Field ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Signal To Noise ◽  
Noise Ratio

In this article, principal component analysis method, which is applied to image compression and feature extraction, is introduced into the dimension reduction of input characteristic variable of support vector regression, and a method of joint estimation of near-field angle and range based on principal component analysis dimension reduction is proposed. Signal-to-noise ratio and calculation amount are the decisive factors affecting the performance of the algorithm. Principal component analysis is used to fuse the main characteristics of training data and discard redundant information, the signal-to-noise ratio is improved, and the calculation amount is reduced accordingly. Similarly, support vector regression is used to model the signal, and the upper triangular elements of the signal covariance matrix are usually used as input features. Since the covariance matrix has more upper triangular elements, training it as a feature input will affect the training speed to some extent. Principal component analysis is used to reduce the dimensionality of the upper triangular element of the covariance matrix of the known signal, and it is used as the input feature of the multi-output support vector regression machine to construct the near-field parameter estimation model, and the parameter estimation of unknown signal is herein obtained. Simulation results show that this method has high estimation accuracy and training speed, and has strong adaptability at low signal-to-noise ratio, and the performance is better than that of the back-propagation neural network algorithm and the two-step multiple signal classification algorithm.

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