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Study on Financial Time Series Prediction Based on Phase Space Reconstruction and Support Vector Machine (SVM)

Hong Zhang
2015 American Journal of Applied Mathematics  
Analyzing and forecasting the financial market based on the theory of phase space reconstruction of support vector regression.  ...  Experiments show that the theory of phase space reconstruction based on support vector regression has a certain degree of predictive ability of market value at risk.  ...  function. 4) training generated training samples using support vector machine to obtain prediction model. 5) using the trained support vector machine forecasting model to forecast.  ... 
doi:10.11648/j.ajam.20150303.16 fatcat:rvyuzs6ppbbrhjcbgekwhiu4ty

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

2020 Regular Issue  
Entities and institutional financiers have gained a lot of growth from financial time series forecasting in recent times.  ...  Researchers in recent times have successfully engaged the application of support vector regression (SVR) to conquer this challenge.  ...  with Kernel Support Vector Regression for Medium-Term Financial Time Series Forecasting RMSE 1 = 1 n t n t AF t A 2 Table 2 Model Selection for Kernel Support Vector Regression 2 Model Selection for  ... 
doi:10.35940/ijmh.g0667.034720 fatcat:fvcw4rgx5ngkxkiaslv7bmewhe

Volatility Forecasting: The Support Vector Regression Can Beat the Random Walk

BEZERRA PEDRO CORREIA SANTOS, ALBUQUERQUE PEDRO HENRIQUE MELO
2019 ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH  
In this paper, we implement a standard Support Vector Regression model with Gaussian and Morlet wavelet kernels on daily returns of two stock market indexes -USA(SP&500) and Brazil (IBOVESPA) -over the  ...  Machine learning techniques that have been employed to forecast financial volatility.  ...  Empirical Modelling According to [1] , the random walk model (RW) is one of the best linear model for financial time series forecasting.  ... 
doi:10.24818/18423264/53.4.19.07 fatcat:tiaq7hpbeja53pgkfcw57drade

Stock Forecasting using M-Band Wavelet-Based SVR and RNN-LSTMs Models [article]

Hieu Quang Nguyen, Abdul Hasib Rahimyar, Xiaodi Wang
2019 arXiv   pre-print
This newly transformed, denoised, and more stable stock data can be followed up by non-parametric statistical methods, such as Support Vector Regression (SVR) and Recurrent Neural Network (RNN) based Long  ...  Through the implementation of these methods, one is left with a more accurate stock forecast, and in turn, increased profits.  ...  Sherman Tao for their continued financial support.  ... 
arXiv:1904.08459v1 fatcat:u3qnsq7dwrdnnbv2xpvi2brcli

Prediction of Stock Market Price using Hybrid of Wavelet Transform and Artificial Neural Network

S. Kumar Chandar, M. Sumathi, S. N. Sivanandam
2016 Indian Journal of Science and Technology  
This paper presents a forecasting model based on Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) for predicting financial time series.  ...  The proposed forecasting model uses the Discrete Wavelet Transform to decompose the financial time series data.  ...  The author uses low-frequency coefficient, support vector machines with different kernels were used as the base line forecasting model.  ... 
doi:10.17485/ijst/2016/v9i8/87905 fatcat:aexjypfx5vfa5exlv55payu4ta

A Hybrid Least Square Support Vector Machine Model with Parameters Optimization for Stock Forecasting

Jian Chai, Jiangze Du, Kin Keung Lai, Yan Pui Lee
2015 Mathematical Problems in Engineering  
This paper proposes an EMD-LSSVM (empirical mode decomposition least squares support vector machine) model to analyze the CSI 300 index.  ...  A WD-LSSVM (wavelet denoising least squares support machine) is also proposed as a benchmark to compare with the performance of EMD-LSSVM.  ...  financial time series.  ... 
doi:10.1155/2015/231394 fatcat:o6kgkiimnrfcljp2hy2p4hudm4

Forecasting volatility based on wavelet support vector machine

Ling-Bing Tang, Ling-Xiao Tang, Huan-Ye Sheng
2009 Expert systems with applications  
One of the challenging problems in forecasting the conditional volatility of stock market returns is that general kernel functions in support vector machine (SVM) cannot capture the cluster feature of  ...  The applicability and validity of wavelet support vector machine (WSVM) for volatility forecasting are confirmed through computer simulations and experiments on realworld stock data.  ...  We also wish to thank the generous support provided by Baden-Wü rttenberg state government in Germany.  ... 
doi:10.1016/j.eswa.2008.01.047 fatcat:a543btwbyvg5jceuanmescrawm

A Big Data Analysis System for Financial Trading

Shian-Chang Huang
2017 GLOBAL BUSINESS & FINANCE REVIEW  
Owing to the high risk associated with trading financial options, this study aims to develop anintelligent option trading support system, where nonlinear or kernel canonical correlation analysis (KCCA)  ...  is used to extract the hidden forces that drive the price movement of an option, and a generalized dynamic kernel based predictors are employed to generate trading signals.  ...  Support Vector Regressions(SVR) The support vector machines (SVM) were proposed by Vapnik (1995) .  ... 
doi:10.17549/gbfr.2017.22.3.32 fatcat:soybthi6izdrva3ab36kz3k5oy

Hybrid Forecasting of Exchange Rate by Using Chaos Wavelet SVM-Markov Model and Grey Relation Degree [article]

Kim Gol, Ri Suk Yun
2012 arXiv   pre-print
We have suggested a wavelet-SVR-Markov forecasting model to predict the finance time series and demonstrated that can more improve the forecasting performance by the rational combination of the forecast  ...  Also we have comprehensively estimated the combination forecast methods according to the forecasting performance indicators.The estimated result have been shown that the combination forecast methods on  ...  [Least-square wavelet Support Vector Machine (LS-WSVM)] In the above, we have made the kernel function of general SVM based on wavelet.  ... 
arXiv:1207.1547v1 fatcat:6asex22yuzbddgylmcavu25x5e

A Hybrid Model for Stream Flow Forecasting Using Wavelet and Least Squares Support Vector Machines

Ani Shabri
2015 Jurnal Teknologi  
This paper proposed a hybrid wavelet-least square support vector machines (WLSSVM) model that combine both wavelet method and LSSVM model for monthly stream flow forecasting.  ...  The original stream flow series was decomposed into a number of sub-series of time series using wavelet theory and these time series were imposed as input data to the LSSVM for stream flow forecasting.  ...  Acknowledgement The authors thankfully acknowledged the financial support that afforded by Universiti Teknologi Malaysia under FRGS Grant (vot. 4F088).  ... 
doi:10.11113/jt.v73.3380 fatcat:lsch63kstzgbxflfzz4wmns4ve

An Evolutionary Functional Link Neural Fuzzy Model for Financial Time Series Forecasting

S. Chakravarty, P. K. Dash, V. Ravikumar Pandi, B. K. Panigrahi
2011 International Journal of Applied Evolutionary Computation  
Slim (2006) has applied neuro fuzzy architecture based on kalman filter to predict financial time series taking Mackey glass time series as experimental data.  ...  Support vector machine (Huang, Nakamori, & Wang, 2004 ) is used to forecast stock movement direction for NIKKEI 225 index.  ... 
doi:10.4018/jaec.2011070104 fatcat:y6ykaolnrzelxcywasw7axjn4a

A Machine Learning Model for Stock Market Prediction [article]

Osman Hegazy, Omar S. Soliman, Mustafa Abdul Salam
2014 arXiv   pre-print
Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on a financial exchange.  ...  Financial time series forecasting based on wavelet kernel support vector was presented in [15] . Computational Intelligence Approaches for Stock Price Forecasting was introduced in [16] .  ...  The Optimization of Share Price Prediction Model Based on Support Vector Machine‫‬ is presented in [14] .  ... 
arXiv:1402.7351v1 fatcat:xq5u5ej6wvh6pa37lzvumtnwpy

Wavelet low- and high-frequency components as features for predicting stock prices with backpropagation neural networks

Salim Lahmiri
2014 Journal of King Saud University: Computer and Information Sciences  
This paper presents a forecasting model that integrates the discrete wavelet transform (DWT) and backpropagation neural networks (BPNN) for predicting financial time series.  ...  The presented model first uses the DWT to decompose the financial time series data.  ...  Support vector machines (SVM) with different kernels and parameters were used as the baseline forecasting model.  ... 
doi:10.1016/j.jksuci.2013.12.001 fatcat:cznhx3ijs5bm7gu6srfowbxhia

Study of Time Series Data Mining for the Real Time Hydrological Forecasting: A Review

Satanand Mishra, C. Saravanan, V. K. Dwivedi
2015 International Journal of Computer Applications  
This paper presents a review of runoff forecasting method based on hydrological time series data mining.  ...  Researchers are developed models for runoff forecasting using the data mining tools and techniques like regression analysis, clustering, artificial neural network (ANN), and support vector machine (SVM  ...  Time series data analysis is a new approach along with weighted support vector machine. Typically nonlinear features are introduced through a nonlinear kernel function.  ... 
doi:10.5120/20692-3581 fatcat:v3vfg7a4wfgwxfdgxdgvlhvrfq

PCA BASED SUPPORT VECTOR MACHINE TECHNIQUE FOR VOLATILITY FORECASTING

R.Seethalakshmi .
2014 International Journal of Research in Engineering and Technology  
Support Vector Machine (SVM) has been applied for volatility estimation of stock market data with limited success, the limitation being in accurate volatility feature predictions due to general kernel  ...  Conditional Volatility of stock market returns is one of the major problems in time series analysis.  ...  ACKNOWLEDGEMENTS Two of the authors wish to thank Dr.K.Kannan, Dean, Humanities and Sciences, for his kind permission to audit the Machine Learning Techniques course handled by him, who in turn wishes  ... 
doi:10.15623/ijret.2014.0308060 fatcat:dcykgs6a7bgzld6clnsfegmrrm
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