| Summary: | Bitcoin price can be challenging to predict due to the presence of non-stationary and non-linear patterns in the data series. It can take time to make accurate predictions in such circumstances. The fact of trends and seasonal fluctuations rarely makes the modeling process more accessible. Therefore, most researchers isolate hidden behavior, such as trends, seasonal and irregular components from the actual series. As a result, information about the data is lost, and the prediction becomes inaccurate. Therefore, this study used the linear structural time series (STS) model to cope with uncertainties by using previous information about the model structure. However, the linearity of the underlying STS model and the accurate knowledge of it are often not available in practice. This is the disadvantage of a single model that cannot handle the different patterns. Therefore, this study proposes the hybrid model STS-NARX to account for non-stationary and non-linear behavior data series. The result shows that the proposed STS-NARX hybrid model has higher accuracy in predicting the Bitcoin price than a single linear STS model due to the greater abundance of information. © 2024 Author(s).
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