scholarly journals Financial Time Series Forecasting Using Empirical Mode Decomposition and Support Vector Regression

Risks ◽  
2018 ◽  
Vol 6 (1) ◽  
pp. 7 ◽  
Author(s):  
Noemi Nava ◽  
Tiziana Matteo ◽  
Tomaso Aste
Keyword(s):  
Time Series ◽  
Support Vector Regression ◽  
Empirical Mode Decomposition ◽  
Financial Time Series ◽  
Time Series Forecasting ◽  
Support Vector ◽  
Financial Time ◽  
Mode Decomposition

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

An Integrated Model Combined ARIMA, EMD with SVR for Stock Indices Forecasting

2016 ◽  
Vol 25 (02) ◽  
pp. 1650005 ◽  
Author(s):  
Heng-Li Yang ◽  
Han-Chou Lin
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Linear Part ◽  
Time Series Forecasting ◽  
Support Vector ◽  
Nonlinear Part ◽  
Financial Time ◽  
Stock Indices ◽  
Mode Decomposition ◽  
Original Time

Financial time series forecasting has become a challenge because it is noisy, non-stationary and chaotic. To overcome this limitation, this paper uses empirical mode decomposition (EMD) to aid the financial time series forecasting and proposes an approach via combining ARIMA and SVR (Support Vector Regression) to forecast. The approach contains four steps: (1) using ARIMA to analyze the linear part of the original time series; (2) EMD is used to decompose the dynamics of the non-linear part into several intrinsic mode function (IMF) components and one residual component; (3) developing a SVR model using the above IMFs and residual components as inputs to model the nonlinear part; (4) combining the forecasting results of linear model and nonlinear model. To verify the effectiveness of the proposed approach, four stock indices are chosen as the forecasting targets. Comparing with some existing state-of-the-art models, the proposed approach gives superior results.

scholarly journals A Trend-Based Segmentation Method and the Support Vector Regression for Financial Time Series Forecasting

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Jheng-Long Wu ◽  
Pei-Chann Chang
Keyword(s):  
Time Series ◽  
Support Vector Regression ◽  
Time Series Data ◽  
Financial Time Series ◽  
Piecewise Linear ◽  
Time Series Forecasting ◽  
Series Data ◽  
Support Vector ◽  
Segmentation Method ◽  
Financial Time

This paper presents a novel trend-based segmentation method (TBSM) and the support vector regression (SVR) for financial time series forecasting. The model is named as TBSM-SVR. Over the last decade, SVR has been a popular forecasting model for nonlinear time series problem. The general segmentation method, that is, the piecewise linear representation (PLR), has been applied to locate a set of trading points within a financial time series data. However, owing to the dynamics in stock trading, PLR cannot reflect the trend changes within a specific time period. Therefore, a trend based segmentation method is developed in this research to overcome this issue. The model is tested using various stocks from America stock market with different trend tendencies. The experimental results show that the proposed model can generate more profits than other models. The model is very practical for real-world application, and it can be implemented in a real-time environment.

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

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