Performance Assessment of ARIMA and LSTM Models in Prediction Using Root Mean Square Error (RMSE)
DOI:
https://doi.org/10.61098/jarcis.v2i1.181Keywords:
Solana, Time Series, ARIMA, LSTM, RMSEAbstract
Cryptocurrency is a digital financial asset that serves as a medium of exchange, with its ownership guaranteed using decentralized cryptographic technology, and it has become a growing investment tool. Solana is one of the highly sought-after Cryptocurrencies by investors. The market price of Solana exhibits highly volatile movements, which are considered risky for investment purposes, as it offers both high potential profits and losses. In this regard, time series data prediction models are used to analyze and forecast the price movements of Solana. By comparing the performance of ARIMA and LSTM models in predicting the closing price of Solana using RMSE as a testing metric, the aim is to determine the efficiency level of both ARIMA and LSTM models. The research results show that the ARIMA model with an order of (2,1,3) achieves an RMSE of 0.019 (1.9%) with an accuracy of 98.1%, while the LSTM model with a data training ratio of 70:30%, a batch size of 64, and 500 epochs has an RMSE of 0.075 (7.5%) with an accuracy of 92.5%. The conclusion drawn from the conducted experiments is that, in the case of using time series data samples from Solana, the ARIMA method demonstrates higher accuracy compared to the LSTM method.
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References
R. Auer and R. Böhme, “Central bank digital currency: the quest for minimally invasive technology,” BIS Work. Pap., no. 948, 2021, [Online]. Available: www.bis.org.
I. Amsyar, E. Christopher, A. Dithi, A. N. Khan, and S. Maulana, “The challenge of cryptocurrency in the era of the digital revolution: A review of systematic literature,” Aptisi Trans. Technopreneursh., vol. 2, no. 2, pp. 153–159, 2020.
X. Li, X. Wang, T. Kong, J. Zheng, and M. Luo, “From bitcoin to solana–innovating blockchain towards enterprise applications,” in International Conference on Blockchain, 2021, pp. 74–100.
U. A. Haruna, “Developing and Implementing a Cryptocurrency For Gombe State University,” 2024.
G. Minotti, “Cryptocurrencies Price Prediction using LSTM Neural Network model,” 2023.
S. M. Shariff, “Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) network models for forecasting energy consumptions,” Eur. J. Electr. Eng. Comput. Sci., vol. 6, no. 3, pp. 7–10, 2022.
H. Mou and J. Yu, “CNN-LSTM Prediction Method for Blood Pressure Based on Pulse Wave,” Electronics, vol. 10, no. 14. 2021, doi: 10.3390/electronics10141664.
F. M. Siemers, C. Feldmann, and J. Bajorath, “Minimal data requirements for accurate compound activity prediction using machine learning methods of different complexity,” Cell Reports Phys. Sci., vol. 3, no. 11, 2022.
A. Lawi, H. Mesra, and S. Amir, “Implementation of Long Short-Term Memory and Gated Recurrent Units on grouped time-series data to predict stock prices accurately,” J. Big Data, vol. 9, no. 1, p. 89, 2022.
A. S. Girsang, “Cryptocurrency Price Prediction Based Social Network Sentiment Analysis Using LSTM-GRU and FinBERT,” IEEE Access, 2023.
P. T. Yamak, L. Yujian, and P. K. Gadosey, “A comparison between arima, lstm, and gru for time series forecasting,” in Proceedings of the 2019 2nd international conference on algorithms, computing and artificial intelligence, 2019, pp. 49–55.
K. Albeladi, B. Zafar, and A. Mueen, “Time Series Forecasting using LSTM and ARIMA,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 1, pp. 313–320, 2023.
G. Abdoli, “Comparing the prediction accuracy of LSTM and ARIMA models for time-series with permanent fluctuation,” Periódico do Núcleo Estud. e Pesqui. sobre Gênero e DireitovCentro Ciências Jurídicas-Universidade Fed. da Paraíba, vol. 9, 2020.
O. O. Okesola, “Data Mining Methodolgy and its application,” 2021.
E. Tuncer and E. D. Bolat, “Classification of epileptic seizures from electroencephalogram (EEG) data using bidirectional short-term memory (Bi-LSTM) network architecture,” Biomed. Signal Process. Control, vol. 73, p. 103462, 2022.
W. Zhang et al., “LSTM-based analysis of industrial IoT equipment,” IEEE Access, vol. 6, pp. 23551–23560, 2018.
O. Barut, L. Zhou, and Y. Luo, “Multitask LSTM model for human activity recognition and intensity estimation using wearable sensor data,” IEEE Internet Things J., vol. 7, no. 9, pp. 8760–8768, 2020.
E. Persson, “Forecasting Efficiency in Cryptocurrency Markets: A machine learning case study.” 2022.
T. R. Noviandy et al., “Deep learning-based bitcoin price forecasting using neural prophet,” Ekon. J. Econ., vol. 1, no. 1, pp. 19–25, 2023.
J. I. Piovani, M. E. Rausky, and J. A. Santos, “ Qualitative Research in Issues of the American Journal of Sociology during the Hegemony of The Chicago School ,” Méthod(e)s African Rev. Soc. Sci. Methodol., vol. 1, no. 1–2, pp. 111–123, 2015, doi: 10.1080/23754745.2015.1017279.
Ü. Ağbulut, A. E. Gürel, and Y. Biçen, “Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison,” Renew. Sustain. Energy Rev., vol. 135, p. 110114, 2021.
J. M. Oliva-Lozano, I. Martín-Fuentes, V. Fortes, and J. M. Muyor, “Differences in worst-case scenarios calculated by fixed length and rolling average methods in professional soccer match-play,” Biol. Sport, vol. 38, no. 3, p. 325, 2021.
Y. A. Ünvan and C. Ergenc, “A COMPREHENSIVE COMPARATIVE STUDY OF MACHINE LEARNING MODELS FOR PREDICTING CRYPTOCURRENCY,” Facta Univ. Ser. Electron. Energ., vol. 37, no. 1, pp. 211–227, 2024.
D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,” Peerj Comput. Sci., vol. 7, p. e623, 2021.
M. Elsaraiti and A. Merabet, “A comparative analysis of the arima and lstm predictive models and their effectiveness for predicting wind speed,” Energies, vol. 14, no. 20, p. 6782, 2021.
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