Comparative Analysis of Bitcoin Price Movement Prediction using ARIMA and FBProphet

The rise of Bitcoin and other cryptocurrencies has transformed the financial landscape, especially emerging markets in Indonesia, where adoption rates have grown significantly in recent years. With Indonesia ranked among the top countries in terms of global crypto usage, the demand for innovative strategies to predict Bitcoin price movements has increased. This study compares the ARIMA and Facebook Prophet models for forecasting Bitcoin price trends using historical data from Indodax, one of Indonesia's largest cryptocurrency exchanges, covering the period from 2019 to 2024. Pre-processing involved handling missing data and applying feature engineering techniques such as moving averages and rolling statistics. Results indicate that ARIMA outperformed FBProphet in accuracy, achieving an RMSE of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{2 6, 8 9 6, 5 5 8}$</tex> and MAPE of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1. 8 1 \%}$</tex>. While FBProphet excelled in capturing seasonal patterns despite its higher RMSE and MAPE, ARIMA demonstrated superior precision but struggled with high market volatility. This study highlights the complementary strengths of both models and provides insights to enhance cryptocurrency price prediction in dynamic markets such as Indonesia.

Authors

Institutions

Publication Details

Published
2025-06-24
DOI
https://doi.org/10.1109/icad65464.2025.11114051
Primary Topic
Vehicle License Plate Recognition
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Comparative Analysis of Bitcoin Price Movement Prediction using ARIMA and FBProphet

Tuga Mauritsius, Veronica Dwiyanti Witak Keluli
1 links
Vehicle License Plate Recognition
article

Comparative Analysis of Bitcoin Price Movement Prediction using ARIMA and FBProphet

Tuga Mauritsius, Veronica Dwiyanti Witak Keluli
article en

Abstract

The rise of Bitcoin and other cryptocurrencies has transformed the financial landscape, especially emerging markets in Indonesia, where adoption rates have grown significantly in recent years. With Indonesia ranked among the top countries in terms of global crypto usage, the demand for innovative strategies to predict Bitcoin price movements has increased. This study compares the ARIMA and Facebook Prophet models for forecasting Bitcoin price trends using historical data from Indodax, one of Indonesia's largest cryptocurrency exchanges, covering the period from 2019 to 2024. Pre-processing involved handling missing data and applying feature engineering techniques such as moving averages and rolling statistics. Results indicate that ARIMA outperformed FBProphet in accuracy, achieving an RMSE of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{2 6, 8 9 6, 5 5 8}$</tex> and MAPE of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1. 8 1 \%}$</tex>. While FBProphet excelled in capturing seasonal patterns despite its higher RMSE and MAPE, ARIMA demonstrated superior precision but struggled with high market volatility. This study highlights the complementary strengths of both models and provides insights to enhance cryptocurrency price prediction in dynamic markets such as Indonesia.

Binus University (ID)
Decent work and economic growth
Openalex Percentile: Top 70%
Vehicle License Plate Recognition
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.