Skip to Main content Skip to Navigation
Journal articles

Defining and applying prediction performance metrics on a recurrent NARX time series model.

Abstract : Nonlinear autoregressive moving average with exogenous inputs (NARMAX) models have been successfully demonstrated for modeling the input-output behavior of many complex systems. This paper deals with the proposition of a scheme to provide time series prediction. The approach is based on a recurrent NARX model obtained by linear combination of a recurrent neural network (RNN) output and the real data output. Some prediction metrics are also proposed to assess the quality of predictions. This metrics enable to compare different prediction schemes and provide an objective way to measure how changes in training or prediction model (Neural network architecture) affect the quality of predictions. Results show that the proposed NARX approach consistently outperforms the prediction obtained by the RNN neural network.
Document type :
Journal articles
Complete list of metadatas

Cited literature [46 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-00501643
Contributor : Martine Azema <>
Submitted on : Monday, July 12, 2010 - 3:11:23 PM
Last modification on : Thursday, November 12, 2020 - 9:42:03 AM
Long-term archiving on: : Thursday, October 14, 2010 - 3:33:46 PM

File

NEUROCOM_Ryad_Rafael.pdf
Files produced by the author(s)

Identifiers

Citation

Ryad Zemouri, Rafael Gouriveau, Noureddine Zerhouni. Defining and applying prediction performance metrics on a recurrent NARX time series model.. Neurocomputing, Elsevier, 2010, 73 (13-15), pp.2506-2521. ⟨10.1016/j.neucom.2010.06.005⟩. ⟨hal-00501643⟩

Share

Metrics

Record views

339

Files downloads

1477