Extended Kalman Filter In Recurrent Neural Network: USDIDR Forecasting Case Study

https://doi.org/10.22146/ijccs.47802

Muhammad Asaduddin Hazazi(1), Agus Sihabuddin(2*)

(1) Master Program of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(2) Department of Computer Science and Electronics, Universitas Gadjah Mada
(*) Corresponding Author

Abstract


Artificial Neural Networks (ANN) especially Recurrent Neural Network (RNN) have been widely used to predict currency exchange rates. The learning algorithm that is commonly used in ANN is Stochastic Gradient Descent (SGD). One of the advantages of SGD is that the computational time needed is relatively short. But SGD also has weaknesses, including SGD requiring several hyperparameters such as the regularization parameter. Besides that SGD relatively requires a lot of epoch to reach convergence. Extended Kalman Filter (EKF) as a learning algorithm on RNN is used to replace SGD with the hope of a better level of accuracy and convergence rate. This study uses IDR / USD exchange rate data from 31 August 2015 to 29 August 2018 with 70% data as training data and 30% data as test data. This research shows that RNN-EKF produces better convergent speeds and better accuracy compared to RNN-SGD.

Keywords


exchange rates; forecasting; recurrent neural network; stochastic gradient descent; extended Kalman Filter

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DOI: https://doi.org/10.22146/ijccs.47802

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