Improving ARIMA models prediction with designing deep learning hybrid models: A Case Study of Cryptocurrency

Document Type : Research Paper

Authors

1 Professor in Economics, Faculty of Management and Economic, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 2. Assistant Prof, Department of Economic, Faculty of Management and Economic, Science and Research Branch, Islamic Azad University, Tehran, Iran

3 Ph.D. Candidate, Department of Economic, Faculty of Management and Economics, Islamic Azad University, Science and Research Branch, Tehran

10.30465/jnet.2024.47552.2101

Abstract

In this paper, we investigate how novel approaches can improve the predictions made by traditional econometric approaches in the field of forecasting. Autoregressive integrated moving average (ARIMA) is known as one of the most widely used methods for predicting economic and financial time series, providing a good framework, especially for short-term linear predictions of time series. However, the assumption of nonlinear effects in time series and the emergence of novel deep learning algorithms, which can extract complex features of time series and model them, have motivated researchers to examine the predictive power of traditional and novel modeling approaches. In this study, two methods are examined for predicting the prices of the four most valuable cryptocurrencies. ARIMA and three approaches in the field of deep learning, including (RNN, LSTM, and GRU), are investigated. In addition, a hybrid model of deep learning and ARIMA has been introduced, which is a combination of the strengths of both models to increase the accuracy of predictions. The results show that the hybrid models perform better in predicting future time series than each of the ARIMA and deep learning models separately. Also, the ARIMA-GRU model has fewer prediction error values than all estimated models.
JEL classification: C22, C89, G17

Keywords

References

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History

  • Receive Date: 28 November 2023
  • Revise Date: 05 April 2024
  • Accept Date: 12 April 2024

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