The nature of statistical learning theory
The nature of statistical learning theory
Independent component analysis: theory and applications
Independent component analysis: theory and applications
Independent component analysis: algorithms and applications
Neural Networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
An investigation of neural networks for linear time-series forecasting
Computers and Operations Research
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Support Vector Machines for Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mining customer product ratings for personalized marketing
Decision Support Systems - Special issue: Web data mining
Face recognition using independent component analysis and support vector machines
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Credit rating analysis with support vector machines and neural networks: a market comparative study
Decision Support Systems - Special issue: Data mining for financial decision making
Decision Support Systems - Special issue: Data mining for financial decision making
Bond rating using support vector machine
Intelligent Data Analysis
A hybrid model for exchange rate prediction
Decision Support Systems
Independent component analysis-based defect detection in patterned liquid crystal display surfaces
Image and Vision Computing
Predicting going concern opinion with data mining
Decision Support Systems
Incorporating domain knowledge into data mining classifiers: An application in indirect lending
Decision Support Systems
Sales forecasting using extreme learning machine with applications in fashion retailing
Decision Support Systems
Soft sensor modeling based on DICA-SVR
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
An overview of statistical learning theory
IEEE Transactions on Neural Networks
Robust support vector regression networks for function approximation with outliers
IEEE Transactions on Neural Networks
Support vector machine with adaptive parameters in financial time series forecasting
IEEE Transactions on Neural Networks
Electricity price forecasting based on support vector machine trained by genetic algorithm
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Tax forecasting theory and model based on SVM optimized by PSO
Expert Systems with Applications: An International Journal
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
A support vector machine-based model for detecting top management fraud
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Predicting stock index using an integrated model of NLICA, SVR and PSO
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Expert Systems with Applications: An International Journal
A D-GMDH model for time series forecasting
Expert Systems with Applications: An International Journal
A multi-agent system for web-based risk management in small and medium business
Expert Systems with Applications: An International Journal
An efficient CMAC neural network for stock index forecasting
Expert Systems with Applications: An International Journal
Journal of Intelligent Manufacturing
Confidence Weighted Mean Reversion Strategy for Online Portfolio Selection
ACM Transactions on Knowledge Discovery from Data (TKDD)
Prediction of movement direction in crude oil prices based on semi-supervised learning
Decision Support Systems
Online portfolio selection: A survey
ACM Computing Surveys (CSUR)
Time series representation: a random shifting perspective
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
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As financial time series are inherently noisy and non-stationary, it is regarded as one of the most challenging applications of time series forecasting. Due to the advantages of generalization capability in obtaining a unique solution, support vector regression (SVR) has also been successfully applied in financial time series forecasting. In the modeling of financial time series using SVR, one of the key problems is the inherent high noise. Thus, detecting and removing the noise are important but difficult tasks when building an SVR forecasting model. To alleviate the influence of noise, a two-stage modeling approach using independent component analysis (ICA) and support vector regression is proposed in financial time series forecasting. ICA is a novel statistical signal processing technique that was originally proposed to find the latent source signals from observed mixture signals without having any prior knowledge of the mixing mechanism. The proposed approach first uses ICA to the forecasting variables for generating the independent components (ICs). After identifying and removing the ICs containing the noise, the rest of the ICs are then used to reconstruct the forecasting variables which contain less noise and served as the input variables of the SVR forecasting model. In order to evaluate the performance of the proposed approach, the Nikkei 225 opening index and TAIEX closing index are used as illustrative examples. Experimental results show that the proposed model outperforms the SVR model with non-filtered forecasting variables and a random walk model.