Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Neural Networks Approach to the Random Walk Dilemma of Financial Time Series
Applied Intelligence
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Market Index Prediction using Fuzzy Boolean Nets
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Neural Networks in Finance: Gaining Predictive Edge in the Market (Academic Press Advanced Finance Series)
Tuning of the structure and parameters of a neural network using an improved genetic algorithm
IEEE Transactions on Neural Networks
| Hi-index | 0.01 |
This paper presents an new hybrid method for financial time series prediction called GRASPES. It is based on the Greedy Randomized Adaptive Search Procedure(GRASP), which is a multi-start metaheuristic for combinatorial problems, and Evolutionary Strategies (ES) concepts for tuning of the structure and parameters of an Artificial Neural Network (ANN). The work proposed here consists of an ANN trained and adjusted by GRASPES, which is capable to evolve the parameters configuration and the weights of the ANN, searches for the minimum number of relevant time lags for a correct time series representation and found an optimal or sub-optimal forecasting model. An experimental investigation is conducted with the GRASPES with four real world financial time series and the results achieved are discussed and compared, according to five well-known performance measures, to other works reported in the literature, demonstrating the good performance of GRASPES for financial time series forecasting.