An efficient modelling approach for forecasting financial time series data using support vector regression and windowing operators

2015 ◽  
Vol 4 (2) ◽  
pp. 134 ◽  
Author(s):  
Risul Islam Rasel ◽  
Nasrin Sultana ◽  
Phayung Meesad
Keyword(s):  
Time Series ◽  
Support Vector Regression ◽  
Time Series Data ◽  
Financial Time Series ◽  
Series Data ◽  
Support Vector ◽  
Financial Time ◽  
Modelling Approach

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 A Hybrid Algorithm for Forecasting Financial Time Series Data Based on DBSCAN and SVR

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 103 ◽  
Author(s):  
Mengxing Huang ◽  
Qili Bao ◽  
Yu Zhang ◽  
Wenlong Feng
Keyword(s):  
Time Series ◽  
Hybrid Algorithm ◽  
Prediction Models ◽  
Time Series Data ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Variable Parameter ◽  
Series Data ◽  
Support Vector ◽  
Financial Time

Financial prediction is an important research field in financial data time series mining. There has always been a problem of clustering massive financial time series data. Conventional clustering algorithms are not practical for time series data because they are essentially designed for static data. This impracticality results in poor clustering accuracy in several financial forecasting models. In this paper, a new hybrid algorithm is proposed based on Optimization of Initial Points and Variable-Parameter Density-Based Spatial Clustering of Applications with Noise (OVDBCSAN) and support vector regression (SVR). At the initial point of optimization, ε and MinPts, which are global parameters in DBSCAN, mainly deal with datasets of different densities. According to different densities, appropriate parameters are selected for clustering through optimization. This algorithm can find a large number of similar classes and then establish regression prediction models. It was tested extensively using real-world time series datasets from Ping An Bank, the Shanghai Stock Exchange, and the Shenzhen Stock Exchange to evaluate accuracy. The evaluation showed that our approach has major potential in clustering massive financial time series data, therefore improving the accuracy of the prediction of stock prices and financial indexes.

scholarly journals Insurance Customer Authentication Using SVM and Financial Time Series Analysis for Mobile Applications

Webology ◽  
2020 ◽  
Vol 17 (2) ◽  
pp. 945-956
Author(s):  
R. Harikrishnan ◽  
R. Jebakumar ◽  
S. Ganesh Kumar ◽  
Amu tha
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Mobile Applications ◽  
Time Series Data ◽  
Financial Time Series ◽  
Mobile Apps ◽  
Insurance Industry ◽  
Series Data ◽  
Support Vector ◽  
Financial Time

Insurance industry facilitates the users to access the information easily in their jobs without the repetition of password and remember the multiple passwords. Current technology attracts the insurers in authentication process. The identity authentification processes requires the customers to jump through the many hoops, which construct an unpleasant customer experience. The proposed method reduces the challenges in insurance business data using the classification algorithms using the support vector machine (SVM)for the mobile Applications since the growing trend in mobile apps will make it easy for the users. A seasonal variations and correlation in this financial time series data using statistical methods and ultimately generate trading signals for the insurance data. The feature extraction process increases the user security. The classification process improves different level of user identity. The support vector machine increases the data validation process quickly. Finally the proposed work enhances the user authentication process. The frame work is implemented using the matlabR2014 software and results were simulated for mobile apps.

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 An Application of the Associate Hopfield Network for Pattern Matching in Chart Analysis

2021 ◽  
Vol 11 (9) ◽  
pp. 3876
Author(s):  
Weiming Mai ◽  
Raymond S. T. Lee
Keyword(s):  
Time Series ◽  
Pattern Matching ◽  
Time Series Data ◽  
Financial Time Series ◽  
Noisy Data ◽  
Hopfield Neural Network ◽  
Hopfield Network ◽  
Series Data ◽  
Financial Time ◽  
Chart Patterns

Chart patterns are significant for financial market behavior analysis. Lots of approaches have been proposed to detect specific patterns in financial time series data, most of them can be categorized as distance-based or training-based. In this paper, we applied a trainable continuous Hopfield Neural Network for financial time series pattern matching. The Perceptually Important Points (PIP) segmentation method is used as the data preprocessing procedure to reduce the fluctuation. We conducted a synthetic data experiment on both high-level noisy data and low-level noisy data. The result shows that our proposed method outperforms the Template Based (TB) and Euclidean Distance (ED) and has an advantage over Dynamic Time Warping (DTW) in terms of the processing time. That indicates the Hopfield network has a potential advantage over other distance-based matching methods.

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

scholarly journals Multiband Prediction Model for Financial Time Series with Multivariate Empirical Mode Decomposition

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
Md. Rabiul Islam ◽  
Md. Rashed-Al-Mahfuz ◽  
Shamim Ahmad ◽  
Md. Khademul Islam Molla
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Time Series Data ◽  
Financial Time Series ◽  
Arma Model ◽  
Series Data ◽  
Discrete Wavelet ◽  
Multivariate Empirical Mode Decomposition ◽  
Financial Time ◽  
Mode Decomposition

This paper presents a subband approach to financial time series prediction. Multivariate empirical mode decomposition (MEMD) is employed here for multiband representation of multichannel financial time series together. Autoregressive moving average (ARMA) model is used in prediction of individual subband of any time series data. Then all the predicted subband signals are summed up to obtain the overall prediction. The ARMA model works better for stationary signal. With multiband representation, each subband becomes a band-limited (narrow band) signal and hence better prediction is achieved. The performance of the proposed MEMD-ARMA model is compared with classical EMD, discrete wavelet transform (DWT), and with full band ARMA model in terms of signal-to-noise ratio (SNR) and mean square error (MSE) between the original and predicted time series. The simulation results show that the MEMD-ARMA-based method performs better than the other methods.

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