标题：Methods of Forecasting Based on Artificial Neural Networks/ Prognozēšanas metodes, kas balstītas uz mākslīgajiem neironu tīkliem/ Методы прогнозирования, основанные на искусственных нейронных сетях
期刊名称：Information Technology and Management Science
印刷版ISSN：2255-9086
电子版ISSN：2255-9094
出版年度：2014
卷号：17
期号：1
页码：25-31
DOI：10.1515/itms-2014-0003
语种：English
出版社：Walter de Gruyter GmbH
摘要：This article presents an overview of artificial neural network (ANN) applications in forecasting and possible forecasting accuracy improvements. Artificial neural networks are computational models and universal approximators, which can be applied to the time series forecasting with a high accuracy. A great rise in research activities was observed in using artificial neural networks for forecasting. This paper examines multi-layer perceptrons (MLPs) - back-propagation neural network (BPNN), Elman recurrent neural network (ERNN), grey relational artificial neural network (GRANN) and hybrid systems - models that fuse artificial neural network with wavelets and autoregressive integrated moving average (ARIMA).
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