Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Mathematics and Computers in Simulation
A Dynamic Clustering Algorithm Based on PSO and Its Application in Fuzzy Identification
IIH-MSP '06 Proceedings of the 2006 International Conference on Intelligent Information Hiding and Multimedia
Expert Systems with Applications: An International Journal
A distributed PSO-SVM hybrid system with feature selection and parameter optimization
Applied Soft Computing
Forecasting the turning time of stock market based on Markov-Fourier grey model
Expert Systems with Applications: An International Journal
A hybrid particle swarm optimization algorithm for optimal task assignment in distributed systems
Computer Standards & Interfaces
Computers and Operations Research
Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization
Computers and Operations Research
An improved GA and a novel PSO-GA-based hybrid algorithm
Information Processing Letters
A study of particle swarm optimization particle trajectories
Information Sciences: an International Journal
Pattern Recognition Letters
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
A new grey prediction model FGM(1, 1)
Mathematical and Computer Modelling: An International Journal
Computers and Electronics in Agriculture
Applications of extension grey prediction model for power system forecasting
Journal of Combinatorial Optimization
Hi-index | 12.05 |
Optimum prediction is a difficult problem, because there are no optimal models for all forecasting problems. In this paper, the authors attempt to find the high precision prediction for grey forecasting model (GM). Considering that chaotic particle swarm optimization algorithm (CPSO) will not get into local optimum and is easy to implement, the paper develops an approach for grey forecasting model, which is particularly suitable for small sample forecasting, based on chaotic particle swarm optimization and optimal input subset which is a new concept. The input subset of traditional time series consists of the whole original data, but the whole original does not always reflect the internal regularity of time series, so the new optimal subset method is proposed to better reflect the internal characters of time series and improve the prediction precision. The numerical simulation result of financial revenue demonstrates that developed algorithm provides very remarkable results compared to traditional grey forecasting model for small dataset forecasting.