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Chapitre D'ouvrage Année : 2019

Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era

Aleš Zamuda
Vincenzo Crescimanna
Juan Burguillo
Joana Matos Dias
Horacio González-Vélez
Roman Senkerik
Claudia Pop
Tudor Cioara
Ioan Salomie
Andrea Bracciali

Résumé

This chapter surveys the state-of-the-art in forecasting cryptocurrency value by Sentiment Analysis. Key compounding perspectives of current challenges are addressed, including blockchains, data collection, annotation, and filtering, and sentiment analysis metrics using data streams and cloud platforms. We have explored the domain based on this problem-solving metric perspective, i.e., as technical analysis, forecasting, and estimation using a standardized ledger-based technology. The envisioned tools based on forecasting are then suggested, i.e., ranking Initial Coin Offering (ICO) values for incoming cryptocurrencies, trading strategies employing the new Sentiment Analysis metrics, and risk aversion in cryptocurrencies trading through a multi-objective portfolio selection. Our perspective is rationalized on the perspective on elastic demand of computational resources for cloud infrastructures.

Dates et versions

hal-03812707 , version 1 (12-10-2022)

Identifiants

Citer

Aleš Zamuda, Vincenzo Crescimanna, Juan Burguillo, Joana Matos Dias, Katarzyna Wegrzyn-Wolska, et al.. Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era. High-Performance Modelling and Simulation for Big Data Applications, 11400, Springer International Publishing, pp.325-349, 2019, Lecture Notes in Computer Science, 978-3-030-16271-9. ⟨10.1007/978-3-030-16272-6_12⟩. ⟨hal-03812707⟩
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