Adding external factors in time series forecasting. Case study: Ethereum price forecasting

Vera Barberán, José María (2020). Adding external factors in time series forecasting. Case study: Ethereum price forecasting. Thesis (Master thesis), E.T.S. de Ingenieros Informáticos (UPM).

Description

Title: Adding external factors in time series forecasting. Case study: Ethereum price forecasting
Author/s:
  • Vera Barberán, José María
Contributor/s:
  • Boström, Henrik
Item Type: Thesis (Master thesis)
Masters title: Data Science
Date: 2020
Subjects:
Freetext Keywords: Time-series, Forecasting, Pattern-based models, ARIMA, LSTM
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Otro
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

The main thrust of time-series forecasting models in recent years has gone in the direction of pattern-based learning, in which the input variable for the models is a vector of past observations of the variable itself to predict. The most used models based on this traditional pattern-based approach are the autoregressive integrated moving average model (ARIMA) and long short-term memory neural networks (LSTM). The main drawback of the mentioned approaches is their inability to react when the underlying relationships in the data change resulting in a degrading predictive performance of the models. In order to solve this problem, various studies seek to incorporate external factors into the models treating the system as a black box using a machine learning approach which generates complex models that require a large amount of data for their training and have little interpretability. In this thesis, three different algorithms have been proposed to incorporate additional external factors into these pattern-based models, obtaining a good balance between forecast accuracy and model interpretability. After applying these algorithms in a study case of Ethereum price time-series forecasting, it is shown that the prediction error can be efficiently reduced by taking into account these influential external factors compared to traditional approaches while maintaining full interpretability of the model.

More information

Item ID: 65902
DC Identifier: https://oa.upm.es/65902/
OAI Identifier: oai:oa.upm.es:65902
Deposited by: Biblioteca Facultad de Informatica
Deposited on: 21 Jan 2021 08:26
Last Modified: 02 Feb 2021 08:53
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