DLCP2F: a DL-based cryptocurrency price prediction framework
نویسندگان
چکیده
Abstract Cryptocurrencies are distributed digital currencies that have emerged as a consequence of financial technology advancement. In 2017, cryptocurrencies shown huge rise in their market capitalization and popularity. They now employed today’s systems individual investors, corporate firms, big institutions heavily investing them. However, this industry is less stable than traditional currency markets. It can be affected by several legal, sentimental, technical factors, so it highly volatile, dynamic, uncertain, unpredictable, hence, accurate forecasting essential. Recently, cryptocurrency price prediction becomes trending research topic globally. Various machine deep learning algorithms, e.g., Neural Networks (NN), Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM) were utilized to analyze the factors influencing prices accordingly predict This paper suggests five-phase framework for based on two state-of-the-art architectures (i.e., BiLSTM GRU). The current study uses three public real-time datasets from “Yahoo Finance”. learning-based algorithms used forecast popular Bitcoin, Ethereum, Cardano). Grid Search approach hyperparameters optimization processes. Results indicate GRU outperformed algorithm Cardano, respectively. lowest RMSE model was found 0.01711, 0.02662, 0.00852 Experimental results proved significant performance proposed achieves minimum MSE values.
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ژورنال
عنوان ژورنال: Discover Artificial Intelligence
سال: 2022
ISSN: ['2731-0809']
DOI: https://doi.org/10.1007/s44163-022-00036-2