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2024 | OriginalPaper | Buchkapitel

A Deep Learning Approach to Predict Cryptocurrency Price by Evaluating Sentiment and Stock Market Correlations

verfasst von : Miftahul Zannat Maliha, Ananya Subhra Trisha, Abu Mauze Tamzid Khan, Prasoon Das, Muhammad Iqbal Hossain, Rafeed Rahman

Erschienen in: Proceedings of the 2nd International Conference on Big Data, IoT and Machine Learning

Verlag: Springer Nature Singapore

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Abstract

For the technological shift, the advancing epoch toward cryptocurrency intensified the impactful method. Metaverse can originate the base operation into a diversified level. The extension of digital marketing contributes to blockchain technology more. Our research demonstrates attested cryptocurrency price evaluation associated with the stock and sentiment. In our research, we have implemented various techniques to predict cryptocurrency prices. Cryptocurrencies like Bitcoin, Ethereum, and Litecoin are the primary focus of this paper. Our research observes the fluctuation in cryptocurrency prices. In our research procedure, deep learning models like LSTM, GRU, LSTM-GRU hybrid, and ARIMA for time series prediction were implemented. The research provides cogent insights into cryptocurrency price prediction fluidity with the stock price and Twitter sentiment on cryptocurrencies. Additionally, the implementation of the LSTM time series model on the combined data depicts the relationship between stock price, Twitter sentiment, and cryptocurrency price pertinence.

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Metadaten
Titel
A Deep Learning Approach to Predict Cryptocurrency Price by Evaluating Sentiment and Stock Market Correlations
verfasst von
Miftahul Zannat Maliha
Ananya Subhra Trisha
Abu Mauze Tamzid Khan
Prasoon Das
Muhammad Iqbal Hossain
Rafeed Rahman
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-99-8937-9_1

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