DocumentCode
5816
Title
Methodological Advances in Artificial Neural Networks for Time Series Forecasting
Author
Rocio Cogollo, Myladis ; Velasquez, Juan David
Author_Institution
Univ. EAFIT, Medellin, Colombia
Volume
12
Issue
4
fYear
2014
fDate
Jun-14
Firstpage
764
Lastpage
771
Abstract
Objective: The aim of this paper is to analyze the development of new forecasting models based on neural networks. Method: We used the systematic literature review method employing a manual search of papers published on new neural networks models in the time period 2000 to 2010. Results: Only 18 studies meet all the requirements of the inclusion criteria. Of these, only three proposals considered a neural networks model using a process different to the autoregressive. Conclusion: Although studies relating to the application of neural network models were frequently present, we find that the studies proposing new forecasting models based on neural networks with a theoretical support and a systematic procedure for the construction of model, were scarce in the time period 2000-2010.
Keywords
forecasting theory; neural nets; time series; AD 2000-2010; artificial neural networks; inclusion criteria; time series forecasting; Adaptation models; Artificial neural networks; Biological system modeling; Computational modeling; Hidden Markov models; Predictive models; ANFIS; ARIMA; Forecasting; artificial neural networks; nonlinear time series;
fLanguage
English
Journal_Title
Latin America Transactions, IEEE (Revista IEEE America Latina)
Publisher
ieee
ISSN
1548-0992
Type
jour
DOI
10.1109/TLA.2014.6868881
Filename
6868881
Link To Document