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Comparative study of vector autoregression and recurrent neural network applied to bitcoin forecasting
Citation
El-Abdelouarti Alouaret, Zakariae (2017). Comparative study of vector autoregression and recurrent neural network applied to bitcoin forecasting. Thesis (Master thesis), E.T.S. de Ingenieros Informáticos (UPM).
Description
| Title: | Comparative study of vector autoregression and recurrent neural network applied to bitcoin forecasting |
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| Item Type: | Thesis (Master thesis) |
| Masters title: | Inteligencia Artificial |
| Date: | July 2017 |
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| Faculty: | E.T.S. de Ingenieros Informáticos (UPM) |
| Department: | Inteligencia Artificial |
| Creative Commons Licenses: | Recognition - No derivative works - Non commercial |
Abstract
In this thesis we are exploring the prediction of next day Bitcoin (BTC)
price through the usage of Recurrent Neural Networks (RNN) . Our
aim is, by using state-of-the-art techniques, to predict the price of
BTC with higher accuracy than the previous works in the literature.
This thesis uses up to 27 time series, spanning from 03/01/2009 to
28/04/2016, with a granularity of 1 data point per day.
More information
| Item ID: | 47934 |
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| DC Identifier: | https://oa.upm.es/47934/ |
| OAI Identifier: | oai:oa.upm.es:47934 |
| Deposited by: | Biblioteca Facultad de Informatica |
| Deposited on: | 30 Sep 2017 08:09 |
| Last Modified: | 30 Sep 2017 08:09 |
