Comparative Analysis of Recurrent Neural Network ModelsPerformance in Predicting Bitcoin Prices

Comparative Analysis of Recurrent Neural Network ModelsPerformance in Predicting Bitcoin Prices

deep learning;recurrent neural network;LSTM;GRU;bitcoin

Recurrent Neural Network is a Deep Learning algorithm that is commonly used to develop prediction systems. There are many variants of RNN such as RNN itself, Long Short Term Memory (LSTM), and Gated Recurrent Unit, so it is frequently debatable which algorithm from the RNN family has the most optimal efficiency and computation time. When developing a prediction system, sequential data or time-series data is required so that an accurate prediction can be made. Sequential or time-series data involves data arranged in time sequence, such as weather data, financial data, carbon emission data and traffic data recorded over time. This research will be carried out by predicting the three RNN models against historical Bitcoin value data. The research method used is Experimental Design by comparing the performance between the three models on bitcoin value time series data, testing is done by involving hyperparameters such as Tanh, Sigmoid and ReLU activation functions, batch size, and epochs. The aim of this research is to find out which RNN model can produce the most optimal performance andfind out what performance measurescan be used to evaluate and compare the performance between the three models. The results of the study show that LSTM is the most effective model with RMSE 0.012441 and MSE 0.000155 but inefficient because it takes 3 minutes 24 seconds to run the computation, in the meantime Tanh activation function gives the most optimal prediction than Sigmoid and RelU and therefore should be the main candidate to be used with RNN models when predictingBitcoinprices

Zidane Ikkoy Ramadhan1*, Harya Widiputra

https://jurnal.iaii.or.id/index.php/RESTI/article/view/5810/939

Departmentof Informatics, Faculty of Information Technology, Perbanas Institute Jakarta, Jakarta, Indonesia

20-06-2024

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