• 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