Title :
Methodological Advances in Artificial Neural Networks for Time Series Forecasting
Author :
Rocio Cogollo, Myladis ; Velasquez, Juan David
Author_Institution :
Univ. EAFIT, Medellin, Colombia
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;
Journal_Title :
Latin America Transactions, IEEE (Revista IEEE America Latina)
DOI :
10.1109/TLA.2014.6868881