Statistical methods for modelling neural networks

M. C. Medeiros, T. Teräsvirta

Research output: Contribution to journalArticlepeer-review


In this paper modelling time series by single hidden layer feedforward neural network models is considered. A coherent modelling strategy based on statistical inference is discussed. The problems of selecting the variables and the number of hidden units are solved by using statistical model selection criteria and tests. Mis-specification tests for evaluating an estimated neural network model are considered. Forecasting with neural network models is discussed and an application to a real time series is presented.

Original languageEnglish (US)
Pages (from-to)227-235
Number of pages9
JournalInternational Journal of Engineering Intelligent Systems for Electrical Engineering and Communications
Issue number4
StatePublished - Dec 2001
Externally publishedYes


  • Model mis-specification
  • Neural computing
  • Nonlinear forecasting
  • Nonlinear time series
  • Smooth transition autoregression
  • Sunspot series
  • Threshold autoregression

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


Dive into the research topics of 'Statistical methods for modelling neural networks'. Together they form a unique fingerprint.

Cite this