A neural network scheme for long-term forecasting of chaotic time series

dc.creatorMaría del Pilar Gómez Gil
dc.creatorJUAN MANUEL RAMIREZ CORTES
dc.creatorSAUL EDUARDO POMARES HERNANDEZ
dc.creatorVICENTE ALARCON AQUINO
dc.date2011
dc.date.accessioned2023-07-25T16:23:59Z
dc.date.available2023-07-25T16:23:59Z
dc.identifierhttp://inaoe.repositorioinstitucional.mx/jspui/handle/1009/1602
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7806796
dc.descriptionThe accuracy of a model to forecast a time series diminishes as the prediction horizon increases, in particular when the prediction is carried out recursively. Such decay is faster when the model is built using data generated by highly dynamic or chaotic systems. This paper presents a topology and training scheme for a novel artificial neural network, named “Hybrid-connected Complex Neural Network” (HCNN), which is able to capture the dynamics embedded in chaotic time series and to predict long horizons of such series. HCNN is composed of small recurrent neural networks, inserted in a structure made of feed-forward and recurrent connections and trained in several stages using the algorithm back-propagation through time (BPTT). In experiments using a Mackey-Glass time series and an electrocardiogram (ECG) as training signals, HCNN was able to output stable chaotic signals, oscillating for periods as long as four times the size of the training signals. The largest local Lyapunov Exponent (LE) of predicted signals was positive (an evidence of chaos), and similar to the LE calculated over the training signals. The magnitudes of peaks in the ECG signal were not accurately predicted, but the predicted signal was similar to the ECG in the rest of its structure.
dc.formatapplication/pdf
dc.languageeng
dc.publisherSpringer Science+Business Media
dc.relationcitation:Gómez-Gil, P., et al., (2011). A neural network scheme for long-term forecasting of chaotic time series, Neural Processing Letters, Vol. 33, (3): 215–233
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.subjectinfo:eu-repo/classification/Long-term prediction/Long-term prediction
dc.subjectinfo:eu-repo/classification/Hybrid-connected Complex Neural Network/Hybrid-connected Complex Neural Network
dc.subjectinfo:eu-repo/classification/Recurrent neural networks/Recurrent neural networks
dc.subjectinfo:eu-repo/classification/Chaotic time series/Chaotic time series
dc.subjectinfo:eu-repo/classification/ECG modeling/ECG modeling
dc.subjectinfo:eu-repo/classification/Mackey-Glass equation/Mackey-Glass equation
dc.subjectinfo:eu-repo/classification/cti/1
dc.subjectinfo:eu-repo/classification/cti/12
dc.subjectinfo:eu-repo/classification/cti/1203
dc.subjectinfo:eu-repo/classification/cti/1203
dc.titleA neural network scheme for long-term forecasting of chaotic time series
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.audiencestudents
dc.audienceresearchers
dc.audiencegeneralPublic


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