An R implementation of a Recurrent Neural Network Trained by Extended Kalman Filter

Bogdan Oancea, Tudorel Andrei () and Raluca Mariana Dragoescu
Additional contact information
Raluca Mariana Dragoescu: Artifex University of Bucharest

Romanian Statistical Review, 2016, vol. 64, issue 2, 125-133

Abstract: Nowadays there are several techniques used for forecasting with different performances and accuracies. One of the most performant techniques for time series prediction is neural networks. The accuracy of the predictions greatly depends on the network architecture and training method. In this paper we describe an R implementation of a recurrent neural network trained by the Extended Kalman Filter. For the implementation of the network we used the Matrix package that allows efficient vector-matrix and matrix-matrix operations. We tested the performance of our R implementation comparing it with a pure C++ implementation and we showed that R can achieve about 75% of the C++ programs. Considering the other advantages of R, our results recommend R as a serious alternative to classical programming languages for high performance implementations of neural networks.

Keywords: R; neural networks; Extended Kalman Filter (search for similar items in EconPapers)
JEL-codes: C10 C88 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.revistadestatistica.ro/wp-content/uploads/2016/06/RRS2_2016_A11.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:rsr:journl:v:64:y:2016:i:2:p:125-133

Access Statistics for this article

More articles in Romanian Statistical Review from Romanian Statistical Review Contact information at EDIRC.
Bibliographic data for series maintained by Adrian Visoiu ().