Predicting bitcoin cryptocurrency price behavior based on ARIMA and NNAR modelling

The development of models to predict the behavior of the Bitcoin cryptocurrency, using a public database (Yahoo! Finance) to predict price trends. The models used were ARIMA and NNAR with the validation of the models being carried out based on the daily closing values of the asset. Both models did not differ significantly, however the adjusted model NNAR (2.2) had a slightly better fit to the original data series, presenting an MPE (Mean Percentage Error) of -0.102.

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Publication Details

Journal
Socioeconomic Analytics
Published
2024-12-20
DOI
https://doi.org/10.51359/2965-4661.2024.265073
Primary Topic
Stock Market Forecasting Methods
Type
article
Field-Weighted Citation Impact
0.00

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article

Predicting bitcoin cryptocurrency price behavior based on ARIMA and NNAR modelling

David Venâncio da Cruz, Patricia Virginia de Santana Lima, Albaro Ramon Paiva Sanz
Socioeconomic Analytics
Stock Market Forecasting Methods
article

Predicting bitcoin cryptocurrency price behavior based on ARIMA and NNAR modelling

David Venâncio da Cruz, Patricia Virginia de Santana Lima, Albaro Ramon Paiva Sanz
article en

Abstract

The development of models to predict the behavior of the Bitcoin cryptocurrency, using a public database (Yahoo! Finance) to predict price trends. The models used were ARIMA and NNAR with the validation of the models being carried out based on the daily closing values of the asset. Both models did not differ significantly, however the adjusted model NNAR (2.2) had a slightly better fit to the original data series, presenting an MPE (Mean Percentage Error) of -0.102.

Socioeconomic AnalyticsVol. 2(1)
Universidade Estadual da Paraíba (BR)
Universidade Estadual da Paraíba
Openalex Percentile: Top 70%
Stock Market Forecasting Methods
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Predicting bitcoin cryptocurrency price behavior based on ARIMA and NNAR modelling — David Venâncio da Cruz, Patricia Virginia de Santana Lima, et al. · Socioeconomic Analytics (2024) | TGRS Research Map | TGRS