Ether Price Prediction Using Advanced Deep Learning Models

Politis, A.K.; Doka, Katerina; Koziris, Nectarios

doi:10.1109/icbc51069.2021.9461061

Cited by 25 publications

(7 citation statements)

References 10 publications

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“…In addition to predicting the price of Bitcoin, there are many studies using LSTM to predict other digital currencies (Sebastião and Godinho 2021;Saadah and Whafa 2020;Derbentsev et al 2020). Politis et al (2021) used LSTM to predict the price of Ether with an accuracy of 84.2%. Livieris et al (2021) used hybrid CNN-LSTM to conduct prediction experiments on Bitcoin (BTC), Ethereum (ETH), and Ripple (XRP) with the highest market value at the time and obtained BTC The prediction accuracy of 55.03% is higher than ETH's 51.51% and XRP's 49.61%.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Bitcoin Price Prediction Using Machine Learning

Chen

2023

JRFM

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The research purpose of this paper is to obtain an algorithm model with high prediction accuracy for the price of Bitcoin on the next day through random forest regression and LSTM, and to explain which variables have influence on the price of Bitcoin. There is much prior literature on Bitcoin price prediction research, and the research methods mainly revolve around the ARMA model of time series and the LSTM algorithm of deep learning. Although it cannot be proved by the Diebold–Mariano test that the prediction accuracy of random forest regression is significantly better than that of LSTM, the prediction errors RMSE and MAPE of random forest regression are better than those of LSTM. The changes in the variables that determine the price of Bitcoin in each period are also obtained through random forest regression. From 2015 to 2018, three US stock market indexes, NASDAQ, DJI, and S&P500 and oil price, and ETH price have impact on Bitcoin prices. Since 2018, the important variables have become ETH price and Japanese stock market index JP225. The relationship between accuracy and the number of periods of explanatory variables brought into the model shows that for predicting the price of Bitcoin for the next day, the model with only one lag of the explanatory variables has the best prediction accuracy.

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Section: Related Workmentioning

confidence: 99%

Analysis of Bitcoin Price Prediction Using Machine Learning

Chen

2023

JRFM

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“…In [12], first, the XGBoost and Random Forest methods are used to select the appropriate features to predict the future price of Ethereum. Then, a set of deep learning models consisting of LSTM, GRU, and TCN is presented to predict the price and price trend of Ethereum in the short and long term (one and seven days, respectively).…”

Section: Use Of Price Data With Technical Analysis Fundamental or Blo...mentioning

confidence: 99%

Cryptocurrencies investment framework using sentiment analysis of Twitter influencers

Chahooki

Jahanbin

Sutikno

2023

IJEECS

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<span>In recent years, cryptocurrency technology has become an attractive area for investment due to its transparency, independence, and non-transactional nature. Many analysts and researchers talk daily on social media about the future of various cryptocurrencies. These ideas can significantly impact whether or not people are willing to invest. This paper provides a framework to help traders learn about the opinions of influential people and organizations in the field. Over the course of six months, the sentiment of more than 90 significant Twitter users was extracted for the proposed framework. In this study, we used the Vader open-source tool for sentiment analysis. This paper provides an excellent opportunity for investment through sentiment analysis<em> </em>of lesser-known or emerging cryptocurrencies. Also in this paper, we introduce the user importance factor to calculate the value of each tweet based on the number of retweets and comments. This factor shows the importance of their opinions instead of considering the number of followers of the authors. This factor causes a lower coefficient to be assigned to an author's opinion if it decreases in importance over time. The results show that in the short and long term, users' opinions are very effective in the market for cryptocurrencies and in predicting its price </span> trend.

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“…It offers valuable information for financial decision-makers and stakeholders. In the domain of Ethereum (Ether) price prediction, Politis et al [8] explored advanced deep-learning models for forecasting Ether's price movements in 2021.…”

Section: Literature Reviewmentioning

confidence: 99%

Unveiling Ethereum’s Future: LSTM-Based Price Prediction and a Systematic Blockchain Analysis

Likhitha,

Raj,

Ul Islam

2023

E3S Web Conf.

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Cryptocurrency has emerged as a revolutionary innovation that has been replacing traditional finances and enthralling the worldwide technology landscape. This has gained a lot of popularity worldwide for its potential to enable peer-to-peer transactions and offer opportunities for investment and novelty. Nevertheless, it gives rise to issues concerning regulatory adherence, instability, and security apprehensions, turning them into a topic of continuous evaluation and investigation within the fields of finance and technology. This research paper presents a comprehensive exploration of the historical evolution of “Ethereum” as one of the leading blockchain platforms, with a primary focus on price prediction using a long-short-term memory (LSTM) machine learning model. The study includes various critical aspects of Ethereum, starting from its historical evolution to its potential future scope in scaling solutions and payments, and also covering the insights of Ethereum’s tokenomics, utility, and beyond. In addition, the methodology involves using the LSTM model to analyze data from Ethereum. The accuracy of price predictions is assessed by evaluating error metrics and further improved by visualizing the data through graphs that show indicators. This paper gives an in-depth perspective for anyone who is seeking a holistic understanding of cryptocurrencies, mainly concentrated on Ethereum, and also provides valuable guidance to investors, developers, and enthusiasts, encouraging them to make knowledgeable decisions in the everchanging blockchain ecosystem.

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Ether Price Prediction Using Advanced Deep Learning Models

Cited by 25 publications

References 10 publications

Analysis of Bitcoin Price Prediction Using Machine Learning

Analysis of Bitcoin Price Prediction Using Machine Learning

Cryptocurrencies investment framework using sentiment analysis of Twitter influencers

Unveiling Ethereum’s Future: LSTM-Based Price Prediction and a Systematic Blockchain Analysis

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