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Study on Financial Time Series Prediction Based on Phase Space Reconstruction and Support Vector Machine (SVM)
2015
American Journal of Applied Mathematics
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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
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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
2019
ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH
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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]
2019
arXiv
pre-print
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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
2016
Indian Journal of Science and Technology
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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
2015
Mathematical Problems in Engineering
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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
2009
Expert systems with applications
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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
2017
GLOBAL BUSINESS & FINANCE REVIEW
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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]
2012
arXiv
pre-print
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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
2015
Jurnal Teknologi
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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
2011
International Journal of Applied Evolutionary Computation
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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]
2014
arXiv
pre-print
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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
2014
Journal of King Saud University: Computer and Information Sciences
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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
2015
International Journal of Computer Applications
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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
2014
International Journal of Research in Engineering and Technology
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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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