{"id":{"repo_id":"venda","oai_identifier":"oai:univendspace.univen.ac.za:11602/3103"},"canonical_url":"https://search.dev.ndltd.org/etd/venda/oai:univendspace.univen.ac.za:11602/3103","repository":{"repo_id":"venda","name":"University of Venda","base_url":"https://univendspace.univen.ac.za/server/oai/request"},"display":{"title":"Predicting price volatility crytocurrency ethereum","abstract":"Volatility is essential when trading or investing in cryptocurrency Ethereum. Over the years, investors, traders and investment banks have found it difficult to predict the price volatility of Ethereumdue to its rapid price fluctuation. This report focuses on forecasting the price volatility of Ethereum for the next two days using daily historical observations of the price of Ethereumobtained from Coindesk and tweets extracted from Twitter ranging from the 1st of August 2022 to the 8th of August 2022. Two models are used to compute the forecast for the next two days: support vector regression and recurrent neural network. The main evaluationmetric used is the mean absolute error. In this study, according to MAE, RNN without tweets forecasts outperformthe SVR model without tweets forecasts, with the best model being the RNN without tweets producing an MAE of 0.0309.","abstract_html":"Volatility is essential when trading or investing in cryptocurrency Ethereum. Over the years, investors, traders and investment banks have found it difficult to predict the price volatility of Ethereumdue to its rapid price fluctuation. This report focuses on forecasting the price volatility of Ethereum for the next two days using daily historical observations of the price of Ethereumobtained from Coindesk and tweets extracted from Twitter ranging from the 1st of August 2022 to the 8th of August 2022. Two models are used to compute the forecast for the next two days: support vector regression and recurrent neural network. The main evaluationmetric used is the mean absolute error. In this study, according to MAE, RNN without tweets forecasts outperformthe SVR model without tweets forecasts, with the best model being the RNN without tweets producing an MAE of 0.0309.","abstract_has_math":false,"creators":["Rambevha, Vhukhudo Ronny"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Sigauke, Caston","Ravele, Thakhani"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-09-05","date_published":"2025-09-05","updated_at":"2026-07-27T21:57:42Z","subjects":["Crytocurrency","Ethereum","Recurrent neural network","Support vector regression","Volatility forecasting","UCTD"],"languages":["en"],"rights":["University of Venda"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://univendspace.univen.ac.za/handle/11602/3103","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Sigauke, Caston","Ravele, Thakhani"]},{"key":"dc:creator","label":"Author","values":["Rambevha, Vhukhudo Ronny"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023"]},{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-01-24T12:05:48Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-01-24T12:05:48Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-09-05"]},{"key":"dc:relation","label":"Dc Relation","values":["PDF"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Crytocurrency","Ethereum","Recurrent neural network","Support vector regression","Volatility forecasting","UCTD"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["University of Venda"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://univendspace.univen.ac.za/handle/11602/3103"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["MSc (e-Science)","Department of Mathematical and Computational Sciences"]},{"key":"dc:description.abstract","label":"Abstract","values":["Volatility is essential when trading or investing in cryptocurrency Ethereum. Over the years, investors, traders and investment banks have found it difficult to predict the price volatility of Ethereumdue to its rapid price fluctuation. This report focuses on forecasting the price volatility of Ethereum for the next two days using daily historical observations of the price of Ethereumobtained from Coindesk and tweets extracted from Twitter ranging from the 1st of August 2022 to the 8th of August 2022. Two models are used to compute the forecast for the next two days: support vector regression and recurrent neural network. The main evaluationmetric used is the mean absolute error. In this study, according to MAE, RNN without tweets forecasts outperformthe SVR model without tweets forecasts, with the best model being the RNN without tweets producing an MAE of 0.0309."]},{"key":"dc:title","label":"Title","values":["Predicting price volatility crytocurrency ethereum"]}]}],"canonical_facts":{"dc:contributor.advisor":["Sigauke, Caston","Ravele, Thakhani"],"dc:creator":["Rambevha, Vhukhudo Ronny"],"dc:date":["2023"],"dc:date.accessioned":["2026-01-24T12:05:48Z"],"dc:date.available":["2026-01-24T12:05:48Z"],"dc:date.issued":["2025-09-05"],"dc:description":["MSc (e-Science)","Department of Mathematical and Computational Sciences"],"dc:description.abstract":["Volatility is essential when trading or investing in cryptocurrency Ethereum. Over the years, investors, traders and investment banks have found it difficult to predict the price volatility of Ethereumdue to its rapid price fluctuation. This report focuses on forecasting the price volatility of Ethereum for the next two days using daily historical observations of the price of Ethereumobtained from Coindesk and tweets extracted from Twitter ranging from the 1st of August 2022 to the 8th of August 2022. Two models are used to compute the forecast for the next two days: support vector regression and recurrent neural network. The main evaluationmetric used is the mean absolute error. In this study, according to MAE, RNN without tweets forecasts outperformthe SVR model without tweets forecasts, with the best model being the RNN without tweets producing an MAE of 0.0309."],"dc:identifier.uri":["https://univendspace.univen.ac.za/handle/11602/3103"],"dc:language.iso":["en"],"dc:relation":["PDF"],"dc:rights":["University of Venda"],"dc:subject":["Crytocurrency","Ethereum","Recurrent neural network","Support vector regression","Volatility forecasting","UCTD"],"dc:title":["Predicting price volatility crytocurrency ethereum"],"dc:type":["Dissertation"]},"updated_at":"2026-07-27T21:57:42Z"}