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Bitcoin Price Prediction Using Deep Learning

EasyChair Preprint no. 7558

8 pagesDate: March 13, 2022


Bitcoin has recently received a lot of attention from the media and the public due to its recent price surge
and crash. Correspondingly, many researchers have investigated various factors that affect the Bitcoin price and the patterns behind its fluctuations, in particular, using various machine learning methods. In this paper, we study and compare various state-of-the-art deep learning methods such as a deep neural network (DNN), a long short-term memory (LSTM) model, Gated recurrent unit (GRU) model, a fast implementation of GRU backed by cuDNN, and Recurrent Neural Network(RNN) for Bitcoin price prediction. Experimental results showed that although LSTM-based prediction models slightly outperformed the other prediction models for Bitcoin price prediction (regression), DNN-based models performed the best for price ups and downs prediction (classification). Overall, the performances of the proposed deep learning-based prediction
models were comparable

Keyphrases: Bitcoin price prediction, CuDNNGRU, GRU, LSTM, Simple RNN

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Anas Saifi},
  title = {Bitcoin Price Prediction Using Deep Learning},
  howpublished = {EasyChair Preprint no. 7558},

  year = {EasyChair, 2022}}
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