Modi, Parth Daxesh ; Arshi, Kamyar ; Kunz, Pertami J. ; Zoubir, Abdelhak M. (2023)
A Data-driven Deep Learning Approach for Bitcoin Price Forecasting.
24th International Conference on Digital Signal Processing. Rhodes, Greece (11.06.2023-13.06.2023)
doi: 10.1109/DSP58604.2023.10167930
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Bitcoin as a cryptocurrency has been one of the most important digital coins and the first decentralized digital currency. We propose a shallow Bidirectional-LSTM (Bi-LSTM) model, fed with feature engineered data using our proposed method to forecast bitcoin closing prices in a daily time frame. We compare the performance with that of other forecasting methods, and show that with the help of the proposed feature engineering method, a shallow deep neural network out-performs other popular price forecasting models.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Modi, Parth Daxesh ; Arshi, Kamyar ; Kunz, Pertami J. ; Zoubir, Abdelhak M. |
| Art des Eintrags: | Bibliographie |
| Titel: | A Data-driven Deep Learning Approach for Bitcoin Price Forecasting |
| Sprache: | Englisch |
| Publikationsjahr: | 5 Juli 2023 |
| Verlag: | IEEE |
| Buchtitel: | 24th DSP 2023: 2023 24th International Conference on Digital Signal Processing |
| Veranstaltungstitel: | 24th International Conference on Digital Signal Processing |
| Veranstaltungsort: | Rhodes, Greece |
| Veranstaltungsdatum: | 11.06.2023-13.06.2023 |
| DOI: | 10.1109/DSP58604.2023.10167930 |
| Kurzbeschreibung (Abstract): | Bitcoin as a cryptocurrency has been one of the most important digital coins and the first decentralized digital currency. We propose a shallow Bidirectional-LSTM (Bi-LSTM) model, fed with feature engineered data using our proposed method to forecast bitcoin closing prices in a daily time frame. We compare the performance with that of other forecasting methods, and show that with the help of the proposed feature engineering method, a shallow deep neural network out-performs other popular price forecasting models. |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Signalverarbeitung Exzellenzinitiative Exzellenzinitiative > Graduiertenschulen Exzellenzinitiative > Graduiertenschulen > Graduate School of Computational Engineering (CE) |
| Hinterlegungsdatum: | 10 Jul 2023 10:22 |
| Letzte Änderung: | 10 Jul 2023 10:22 |
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