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All Journal JOIV : International Journal on Informatics Visualization
Megat Syahirul Amin Megat Ali
Microwave Research Institute, Universiti Teknologi MARA, Malaysia
Computer Science & IT

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MLP-NARX Bitcoin Price Prediction Model Integrating System Identification Modelling Principles Muhammad Nazrin Farhan Nasarudin; Ihsan Mohd Yassin; Megat Syahirul Amin Megat Ali; Mohd Khairil Adzhar Mahmood; Rahimi Baharom; Zairi Ismael Rizman
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.943

Abstract

Bitcoin is a decentralized digital currency that enables people to exchange value without requiring a third-party intermediary. Due to its many advantages, it has received much interest from institutional and individual investors. Despite its meteoric increase, the price of Bitcoin extremely volatile asset class as it purely relies on supply and demand. This presents an interesting opportunity to create a forecasting model. However, many research papers in this area does not analyse the residuals as part of the forecasting resulting in potentially biased models. In this paper, we demonstrate System Identification (SI) residual analysis techniques to the analysis of our forecasting model. The Multi-Layer Perceptron (MLP) Nonlinear Autoregressive with Exogeneous Inputs (NARX) uses historical price data and several technical indicators to predict the future price movements of Bitcoin. The Particle Swarm Optimization (PSO) algorithm was used to find optimal parameters for the model. The model was able to predict one day ahead price in the prediction test. The model has successfully captured the dynamics of the data through the tests performed on residuals. It is also proving the randomness of residuals, albeit some minor violations.
Co-Authors Ihsan Mohd Yassin Mohd Khairil Adzhar Mahmood Muhammad Nazrin Farhan Nasarudin Rahimi Baharom Zairi Ismael Rizman