Chaotic time series prediction with employment of ant colony optimization

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.

► Novel method based on ant colony optimization to predict chaotic series is proposed.
► The method allows forecast series for periods comparable with horizon of prognosis.
► The method was tested by standard benchmarks (Lorenz system, Mackey–Glass equation).
► The method allows extract parts of series bad and good for prognosis.