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

Author

Listed:
  • Bogdan Oancea

    (University of Bucharest)

  • Tudorel Andrei

    (The Bucharest University of Economic Studies)

  • Raluca Mariana Dragoescu

    (Artifex University of Bucharest)

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.

Suggested Citation

  • Bogdan Oancea & Tudorel Andrei & Raluca Mariana Dragoescu, 2016. "An R implementation of a Recurrent Neural Network Trained by Extended Kalman Filter," Romanian Statistical Review, Romanian Statistical Review, vol. 64(2), pages 125-133, June.
    • Handle: RePEc:rsr:journl:v:64:y:2016:i:2:p:125-133
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    References listed on IDEAS

    as
    1. Bogdan Oancea & Tudorel Andrei & Raluca Mariana Dragoescu, 2015. "Accelerating R with high performance linear algebra libraries," Romanian Statistical Review, Romanian Statistical Review, vol. 63(3), pages 109-117, September.
    2. Oancea, Bogdan & Dragoescu, Raluca & Ciucu, Stefan, 2013. "Predicting students’ results in higher education using a neural network," MPRA Paper 72041, University Library of Munich, Germany.
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    More about this item

    Keywords

    R; neural networks; Extended Kalman Filter;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software

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