Crypto Currency Price Prediction on Ethereum Using Time Series Forecasting Models Arima and Facebook Prophet Models

DOI

10.2991/978-94-6463-471-6_102How to use a DOI?

Keywords

Ethereum; ARIMA; Facebook prophet; MSE

Abstract

Crypto currencies have emerged as a popular investment option in recent years, with Ethereum being one of the most prominent ones. Accurate price prediction of Ethereum can provide valuable insights to investors and traders for making informed decisions. In this study, we utilized two time series prediction models, ARIMA (Auto Regressive Integrated Moving Average) and Facebook Prophet, to predict the price of Ethereum. This research focuses on collecting legacy price data of Ethereum from a reliable source. The data was preprocessed to handle missing values and outliers. ARIMA and Facebook Prophet models were then implemented on the preprocessed data to generate Ethereum price forecasts. The models were trained using a time period of historical data and validated using a hold-out set of data. The MSE, which measures the squared discrepancies between predicted and real Ethereum prices, was used to assess the models’ performance. Lower MSE values indicate better model performance. The results revealed that Facebook Prophet outperformed ARIMA in terms of MSE, indicating superior accuracy in Ethereum price prediction. The higher accuracy of Facebook Prophet may be attributed to it’s ability to handle seasonality, trend changes, and outliers, which are common characteristics of crypto currency price data. In conclusion, this study demonstrates the effectiveness of time series forecasting models, specifically ARIMA and Facebook Prophet, in predicting Ethereum prices. The findings suggest that Facebook Prophet may be a more accurate model compared to ARIMA for Ethereum price prediction, as evidenced lower MSE values. The study provides valuable insights for investors and traders interested in utilizing forecasting models for Ethereum price prediction, and may serve as a basis for further research in this area.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Juvvala Sailaja
AU  - Kovvuri N. Bhargavi
AU  - G. L. Narasamba Vanguri
AU  - Nagireddi Suryakala
AU  - Srinivasulu Thiruveedula
PY  - 2024
DA  - 2024/07/30
TI  - Crypto Currency Price Prediction on Ethereum Using Time Series Forecasting Models Arima and Facebook Prophet Models
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 1074
EP  - 1084
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_102
DO  - 10.2991/978-94-6463-471-6_102
ID  - Sailaja2024
ER  -