Forecasting enrollments based on fuzzy time series
Fuzzy Sets and Systems
Artificial Neural Networks: Theory and Applications
Artificial Neural Networks: Theory and Applications
Genetic Algorithms and Genetic Programming in Computational Finance
Genetic Algorithms and Genetic Programming in Computational Finance
Empirical Bayes approach to block wavelet function estimation
Computational Statistics & Data Analysis
An intelligent system for customer targeting: a data mining approach
Decision Support Systems
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Journal of Global Optimization
Long-term forecasting of Internet backbone traffic
IEEE Transactions on Neural Networks
Application of type-2 neuro-fuzzy modeling in stock price prediction
Applied Soft Computing
Low cost remote gaze gesture recognition in real time
Applied Soft Computing
Genetic algorithms for a two-agent single-machine problem with release time
Applied Soft Computing
Swarm intelligence approaches to estimate electricity energy demand in Turkey
Knowledge-Based Systems
An artificial bee colony algorithm for the maximally diverse grouping problem
Information Sciences: an International Journal
Artificial bee colony algorithm: a survey
International Journal of Advanced Intelligence Paradigms
Expert Systems with Applications: An International Journal
A hybrid metaheuristic for the cyclic antibandwidth problem
Knowledge-Based Systems
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This study presents an integrated system where wavelet transforms and recurrent neural network (RNN) based on artificial bee colony (abc) algorithm (called ABC-RNN) are combined for stock price forecasting. The system comprises three stages. First, the wavelet transform using the Haar wavelet is applied to decompose the stock price time series and thus eliminate noise. Second, the RNN, which has a simple architecture and uses numerous fundamental and technical indicators, is applied to construct the input features chosen via Stepwise Regression-Correlation Selection (SRCS). Third, the Artificial Bee Colony algorithm (ABC) is utilized to optimize the RNN weights and biases under a parameter space design. For illustration and evaluation purposes, this study refers to the simulation results of several international stock markets, including the Dow Jones Industrial Average Index (DJIA), London FTSE-100 Index (FTSE), Tokyo Nikkei-225 Index (Nikkei), and Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). As these simulation results demonstrate, the proposed system is highly promising and can be implemented in a real-time trading system for forecasting stock prices and maximizing profits.