Mackey-Glass noisy chaotic time series prediction by a swarm-optimized neural network

19th Chilean Physics Symposium 2014

López-Caraballo, C.H.^1 ; Salfate, I.^1 ; Lazzús, J.A.^1 ; Rojas, P.^1 ; Rivera, M.^1 ; Palma-Chilla, L.^1

Departamento de Física y Astronomía, Universidad de la Serena, Avda. J. Cisternas 1200, Casilla La Serena

554, Chile^1

关键词: Chaotic behaviour;    Chaotic time series;    Chaotic time series prediction;    Long-term forecast;    Long-term prediction;    Performance prediction;    Stochastic procedure;    Time series prediction;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/720/1/012002/pdf
DOI  :  10.1088/1742-6596/720/1/012002

来源: IOP

【 摘 要 】

In this study, an artificial neural network (ANN) based on particle swarm optimization (PSO) was developed for the time series prediction. The hybrid ANN+PSO algorithm was applied on Mackey-Glass noiseless chaotic time series in the short-term and long-term prediction. The performance prediction is evaluated and compared with similar work in the literature, particularly for the long-term forecast. Also, we present properties of the dynamical system via the study of chaotic behaviour obtained from the time series prediction. Then, this standard hybrid ANN+PSO algorithm was complemented with a Gaussian stochastic procedure (called stochastic hybrid ANN+PSO) in order to obtain a new estimator of the predictions that also allowed us compute uncertainties of predictions for noisy Mackey-Glass chaotic time series. We study the impact of noise for three cases with a white noise level (σN) contribution of 0.01, 0.05 and 0.1.