Rashid, Nurazlina Abdul (2025) Hybridizing Structural Time Series With Dynamic Nonlinear Autoregressive Neural Networks To Improve Cryptocurrency Price Prediction. PhD thesis, Perpustakaan Hamzah Sendut.
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Abstract
This study proposes an alternative hybrid model that combines structural time series (sts) with dynamic nonlinear autoregressive neural networks (nar and narx) to improve cryptocurrency price predictions. Given that cryptocurrency market movements are complex, caused by volatility, nonstationarity, and nonlinearity, as well as being influenced by hidden and external factors, accurate forecasting presents a challenge, as traditional models often fail to capture the market's complex dynamics. Addressing these limitations, this research integrates sts models, renowned for their ability to handle nonstationarity and modeling hidden factors, including trends, seasonality, irregular components, and external factors, with dynamic nonlinear autoregressive neural network models capable of managing nonlinear patterns. Utilizing a detailed analysis of the top five cryptocurrencies by their market capitalization as of december 2022, the study adopts a multi-stage methodology in order to achieve the research objectives. It begins with identifying initial trend behaviors through regression model, followed by modeling hidden factors using sts. The innovation of this study lies in enhancing these models with hybrid sts-narx and sts-nar frameworks, significantly improving predictive accuracy.
| Item Type: | Thesis (PhD) |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA1 Mathematics (General) |
| Divisions: | Pusat Pengajian Sains Matematik (School of Mathematical Sciences) > Thesis |
| Depositing User: | Mr Hasmizar Mansor |
| Date Deposited: | 13 May 2026 02:41 |
| Last Modified: | 13 May 2026 02:41 |
| URI: | http://eprints.usm.my/id/eprint/64185 |
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