نوع مقاله : علمی- پژوهشی
نویسندگان
1 دانشجوی دکتری اقتصاد گرایش اقتصاد سنجی دانشگاه آزاد واحد علوم و تحقیقات تهران
2 استاد دانشگاه شهید بهشتی تهران
3 استادیار، گروه اقتصاد، دانشکده اقتصاد و مدیریت، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسندگان [English]
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.
کلیدواژهها [English]
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