Publicação
Bitcoin Price Prediction: A Comparative analysis of PatchTST and LSTM
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | pt_PT |
| dc.contributor.advisor | Scott, Ian James | |
| dc.contributor.author | Dutra, Thomás Martino Martins | |
| dc.date.accessioned | 2024-11-18T12:46:39Z | |
| dc.date.available | 2024-11-18T12:46:39Z | |
| dc.date.issued | 2024-10-31 | |
| dc.description | Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence | pt_PT |
| dc.description.abstract | Transformers-based algorithms have revolutionized the field of natural language processing. In this context, these types of algorithms are being adapted to time series forecasting. The present study compares PatchTST, a Transformers-based algorithm, against the commonly used Long Short-Term Memory (LSTM) algorithm in the problem of Bitcoin forecasting. The methodology employed in this study involved the collection of data from blockchain, macroeconomics, and technical indicators. This data was used to predict Bitcoin prices across various lookback and horizon windows. The models were evaluated using standard evaluation metrics to ensure robust performance comparison. The results demonstrated that PatchTST outperformed LSTM in both short and long-term horizons. Specifically, for the short-term horizon, PatchTST achieved a Root Mean Square Error (RMSE) of 10.36 compared to 149.72 for LSTM. For long-term horizons, PatchTST achieved an RMSE of 1 909.96, whereas LSTM had an RMSE of 2 310.41. PatchTST exhibits superior capability in capturing and predicting Bitcoin prices compared to LSTM, making it a more effective algorithm for time series forecasting in the context of cryptocurrency markets. | pt_PT |
| dc.identifier.tid | 203785207 | |
| dc.identifier.uri | http://hdl.handle.net/10362/175428 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Bitcoin | pt_PT |
| dc.subject | Cryptocurrencies | pt_PT |
| dc.subject | Machine Learning | pt_PT |
| dc.subject | Long Short-Term Memory | pt_PT |
| dc.subject | Transformers | pt_PT |
| dc.subject | PatchTST | pt_PT |
| dc.subject | SDG 4 - Quality education | pt_PT |
| dc.subject | SDG 8 - Decent work and economic growth | pt_PT |
| dc.title | Bitcoin Price Prediction: A Comparative analysis of PatchTST and LSTM | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Gestão de Informação, especialização em Gestão do Conhecimento e Inteligência de Negócio | pt_PT |