Statistical Inference for Modeling Neural Network in Multivariate Time Series

  • Dhoriva Urwatul Wutsqa
  • Subanar Subanar
  • Suryo Guritno
  • Zanzawi Soejoeti

Abstract

We present a statistical procedure based on hypothesis test to build neural networks model in multivariate time series case. The method involved strategies for specifying the number of hidden units and the input variables in the model using inference of R2 increment. We draw on forward approach starting from empty model to gain the optimal neural networks model. The empirical study was employed relied on simulation data to examine the effectiveness of inference procedure. The result showed that the statistical inference could be applied successfully for modeling neural networks in multivariate time series analysis.
Published
2008-01-15
How to Cite
URWATUL WUTSQA, Dhoriva et al. Statistical Inference for Modeling Neural Network in Multivariate Time Series. Jurnal ILMU DASAR, [S.l.], v. 9, n. 1, p. 15-21, jan. 2008. ISSN 2442-5613. Available at: <https://jurnal.unej.ac.id/index.php/JID/article/view/180>. Date accessed: 04 july 2020.
Section
General

Keywords

neural networks; R2 increment; multivariate time series