6533b7d6fe1ef96bd1265c4b

RESEARCH PRODUCT

Prediction of Highly Non-stationary Time Series Using Higher-Order Neural Units

Edgar A. Martinez GarciaRicardo Rodríguez JorgeJolanta Mizera-pietraszkoJiri BilaRafael Torres Córdoba

subject

Quadratic equationQuantitative Biology::Neurons and CognitionBasis (linear algebra)Series (mathematics)Artificial neural networkOrder (exchange)Computer scienceSliding window protocolTime seriesSpecial caseAlgorithm

description

Adaptive predictive models can use conventional and nonconventional neural networks for highly non-stationary time series prediction. However, conventional neural networks present a series of known drawbacks. This paper presents a brief discussion about this concern as well as how the basis of higher-order neural units can overcome some of them; it also describes a sliding window technique alongside the batch optimization technique for capturing the dynamics of non-stationary time series over a Quadratic Neural Unit, a special case of higher-order neural units. Finally, an experimental analysis is presented to demonstrate the effectiveness of the proposed approach.

yearjournalcountryeditionlanguage
2017-11-03
10.1007/978-3-319-69835-9_74https://link.springer.com/chapter/10.1007/978-3-319-69835-9_74