The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Support Vector Machine Regression for Volatile Stock Market Prediction
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Bond rating using support vector machine
Intelligent Data Analysis
A TSK type fuzzy rule based system for stock price prediction
Expert Systems with Applications: An International Journal
A type-2 fuzzy rule-based expert system model for stock price analysis
Expert Systems with Applications: An International Journal
A neural-network-based nonlinear metamodeling approach to financial time series forecasting
Applied Soft Computing
Nature-Inspired Metaheuristic Algorithms
Nature-Inspired Metaheuristic Algorithms
Chaos-based support vector regressions for exchange rate forecasting
Expert Systems with Applications: An International Journal
A multiple-kernel support vector regression approach for stock market price forecasting
Expert Systems with Applications: An International Journal
Housing price forecasting based on genetic algorithm and support vector machine
Expert Systems with Applications: An International Journal
SVR with hybrid chaotic genetic algorithms for tourism demand forecasting
Applied Soft Computing
A hybrid stock selection model using genetic algorithms and support vector regression
Applied Soft Computing
Application of type-2 neuro-fuzzy modeling in stock price prediction
Applied Soft Computing
Discovering golden nuggets: data mining in financial application
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Genetically optimized fuzzy polynomial neural networks
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
The evidence framework applied to support vector machines
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Support vector machine with adaptive parameters in financial time series forecasting
IEEE Transactions on Neural Networks
A Hybrid Neurogenetic Approach for Stock Forecasting
IEEE Transactions on Neural Networks
Trend Time–Series Modeling and Forecasting With Neural Networks
IEEE Transactions on Neural Networks
Forecasting method of stock price based on polynomial smooth twin support vector regression
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
Implementing support vector regression with differential evolution to forecast motherboard shipments
Expert Systems with Applications: An International Journal
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Due to the inherent non-linearity and non-stationary characteristics of financial stock market price time series, conventional modeling techniques such as the Box-Jenkins autoregressive integrated moving average (ARIMA) are not adequate for stock market price forecasting. In this paper, a forecasting model based on chaotic mapping, firefly algorithm, and support vector regression (SVR) is proposed to predict stock market price. The forecasting model has three stages. In the first stage, a delay coordinate embedding method is used to reconstruct unseen phase space dynamics. In the second stage, a chaotic firefly algorithm is employed to optimize SVR hyperparameters. Finally in the third stage, the optimized SVR is used to forecast stock market price. The significance of the proposed algorithm is 3-fold. First, it integrates both chaos theory and the firefly algorithm to optimize SVR hyperparameters, whereas previous studies employ a genetic algorithm (GA) to optimize these parameters. Second, it uses a delay coordinate embedding method to reconstruct phase space dynamics. Third, it has high prediction accuracy due to its implementation of structural risk minimization (SRM). To show the applicability and superiority of the proposed algorithm, we selected the three most challenging stock market time series data from NASDAQ historical quotes, namely Intel, National Bank shares and Microsoft daily closed (last) stock price, and applied the proposed algorithm to these data. Compared with genetic algorithm-based SVR (SVR-GA), chaotic genetic algorithm-based SVR (SVR-CGA), firefly-based SVR (SVR-FA), artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS), the proposed model performs best based on two error measures, namely mean squared error (MSE) and mean absolute percent error (MAPE).