Comparative study of vector autoregression and recurrent neural network applied to bitcoin forecasting

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
Author/s:
  • El-Abdelouarti Alouaret, Zakariae
Contributor/s:
  • Mateos Caballero, Alfonso
Item Type: Thesis (Master thesis)
Masters title: Inteligencia Artificial
Date: July 2017
Subjects:
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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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
DC Identifier: http://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
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