A Gated Recurrent Unit Approach to Bitcoin Price Prediction

Dutta, Aniruddha; Kumar, S. Sai; Basu, Meheli

doi:10.2139/ssrn.3514069

Cited by 6 publications

(4 citation statements)

References 51 publications

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“…The workflow initiates by applying feature engineering on the dataset, as a previous study proved that featured engineering on features of the dataset provides promising results [8]. To avoid any bias, feature-wise data scaling is performed on the dataset.…”

Section: Proposed Methodologymentioning

confidence: 99%

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The Prediction of Cryptocurrency Prices Using Neural Architectures and Sentiment Analysis

Ghouri,

Ashraf

2022

VFAST trans. softw. eng.

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Cryptocurrency is the most secure, traceable, and reliable intangible currency because it uses blockchain technology. It eliminates the middle layer of financial institutes in the traditional economic system. Because of high returns in cryptocurrencies, investors and other firms invest a lot of money. But the prices of the cryptocurrencies are uncertain. Prices of cryptocurrencies are influenced by many factors like sentiments, trading volume, and similar. Researchers are doing plenty of work to predict the accurate prices of various cryptocurrencies. However, many of these methods cannot be used in real-time. Several deep learning models such as Neural networks (NN), Long short-term memory (LSTM), and Gated recurrent unit (GRU) have been utilized by researchers for predicting the price of cryptocurrencies and yet, are unable to achieve significant results. This work combines LSTM and GRU with sentiment analysis to precisely estimate bitcoin values. We have used Root means square error (RMSE) to evaluate the model performance with and without sentiments. Empirically, we have compared the results with the other state-of-the-art models and found better results using the proposed hybrid model incorporated with sentiments.

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Section: Proposed Methodologymentioning

confidence: 99%

“…Dutta et al [8] used the GRU (Gated Recurring Unit) with the recurrent dropout and compared its results with the LSTM and RNN. GRU with the recurrent dropout outperforms the other state-of-the-art models.…”

Section: Literature Reviewmentioning

confidence: 99%

The Prediction of Cryptocurrency Prices Using Neural Architectures and Sentiment Analysis

Ghouri,

Ashraf

2022

VFAST trans. softw. eng.

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“…c)GRU: It is a new generation of RNN that is very similar to LSTM. However, instead of using a unit state, the GRU uses a hidden state to transfer information [17].…”

Section: Model Comparisonsmentioning

confidence: 99%

Deep Learning Ethereum Token Price Prediction on Dynamic Network and Time Series Analysis

Ao¹,

Li²,

Yu³

2023

Advances in Computer Science Research

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Ethereum has recently surged in popularity, as it can hold various digital tokens and decentralized applications. This paper aims to predict UNI's price in USD through dynamic network analysis and time-series analysis. Previous research in this field rarely considers comprehensive network analysis while predicting token price. This paper puts forward a strengthened Bidirectional LSTM model that includes token economical features and network features. We use Root Mean Squared Error (RMSE) to verify the validity and compare it with other LSTM and GRU models on performance. Lastly, a logarithm difference method for data preprocessing was introduced to resolve the lag problems.

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“…Every time it alters the unreliable agent without demanding entity related systems [8,9]. Adding to the progressive growth, the interference of BC finance generates a strong influence on orthodox financial goals [10]. These influences effectively gained the attention of researchers who study BC financial products.…”

Section: Introductionmentioning

confidence: 99%

Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning

Kalyani¹,

Velmurugan²,

Abdeljaber³

et al. 2023

Computers, Materials &Amp; Continua

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In recent times, financial globalization has drastically increased in different ways to improve the quality of services with advanced resources. The successful applications of bitcoin Blockchain (BC) techniques enable the stockholders to worry about the return and risk of financial products. The stockholders focused on the prediction of return rate and risk rate of financial products. Therefore, an automatic return rate bitcoin prediction model becomes essential for BC financial products. The newly designed machine learning (ML) and deep learning (DL) approaches pave the way for return rate predictive method. This study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder (JSO-ELMAE) for return rate prediction of BC financial products. The presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial products. Besides, the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification results. The application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return rates. The experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many aspects. The experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.

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A Gated Recurrent Unit Approach to Bitcoin Price Prediction

Cited by 6 publications

References 51 publications

The Prediction of Cryptocurrency Prices Using Neural Architectures and Sentiment Analysis

The Prediction of Cryptocurrency Prices Using Neural Architectures and Sentiment Analysis

Deep Learning Ethereum Token Price Prediction on Dynamic Network and Time Series Analysis

Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning

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