22. Electrocardiogram time series forecasting and optimization using ant colony optimization algorithm
Paulius Čepulionis1, Kristina Lukoševičiūtė2
Kaunas University of Technology, Kaunas, Lithuania
E-mail: firstname.lastname@example.org, email@example.com
(Received 30 April 2016; accepted 2 June 2016)
Abstract. The aim of this work is to create the time series dynamic model, which is based on non‑uniform embedding in the phase-space. To solve selection of time delays problem efficiently, this paper proposes an ant colony optimization (ACO) way. Firstly, false nearest neighbor method is used for determine the embedding dimension. Secondly, ant colony optimization algorithm is used for non-uniform time delay search. To quicken search speed, roulette wheel selection algorithm distributes ants pheromones. Optimization fitness function is the average area of all attractors. Obtained embeddings found by this model are applied in time-series forecasting using radial basis function neural networks. The study is presented in Mackey-Glass and electrocardiogram (ECG) time series forecasting. Prediction results show that the proposed model provides precise prediction accuracy.
Keywords: non-uniform embedding, ant colony optimization, electrocardiogram, time series forecasting.
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Cite this article
Čepulionis Paulius, Lukoševičiūtė Kristina Electrocardiogram time series forecasting and optimization using ant colony optimization algorithm. Mathematical Models in Engineering, Vol. 2, Issue 1, 2016, p. 69‑77.
Mathematical Models in Engineering. June 2016, Volume 2, Issue 1
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