Chaos and Time-Series Analysis
Chaos and Time-Series Analysis
Swarm Intelligence in Data Mining (Studies in Computational Intelligence)
Swarm Intelligence in Data Mining (Studies in Computational Intelligence)
PSO-based single multiplicative neuron model for time series prediction
Expert Systems with Applications: An International Journal
Power load forecasting using support vector machine and ant colony optimization
Expert Systems with Applications: An International Journal
ARFNNs with SVR for prediction of chaotic time series with outliers
Expert Systems with Applications: An International Journal
A locally linear RBF network-based state-dependent AR model for nonlinear time series modeling
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Generalized dynamical fuzzy model for identification and prediction
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
| Hi-index | 12.05 |
In this study, the novel method to predict chaotic time series is proposed. The method employs the ant colony optimization paradigm to analyze topological structure of the attractor behind the given time series and to single out the typical sequences corresponding to the different part of the attractor. The typical sequences are used to predict the time series values. The method was applied to time series generated by the Lorenz system, the Mackey-Glass equation, and weather time series as well. The method is able to provide robust prognosis to the periods comparable with the horizon of prediction.