پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شمارهها | 691 |
| تعداد مقالات | 10,035 |
| تعداد مشاهده مقاله | 71,040,530 |
| تعداد دریافت فایل اصل مقاله | 62,490,121 |
The use of ARIMA, LSTM and GRU models in time series hybridization with practical application | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 106، دوره 14، شماره 1، فروردین 2023، صفحه 1371-1383 اصل مقاله (765.78 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.7110 | ||
| نویسندگان | ||
| Noor Saleem Mohammed Ali؛ Firas Ahmmed Mohammed* | ||
| College of Administration and Economics, Department of Statistics, University of Baghdad, Iraq | ||
| تاریخ دریافت: 12 مرداد 1401، تاریخ بازنگری: 28 شهریور 1401، تاریخ پذیرش: 21 مهر 1401 | ||
| چکیده | ||
| The importance of forecasting has emerged in the economic field in order to achieve economic growth, as forecasting is one of the important topics in the analysis of time series, and accurate forecasting of time series is one of the most important challenges in which we seek to make the best decision. The aim of the research is to suggest the use of hybrid models for forecasting the daily crude oil prices as the hybrid model consists of integrating the linear component, which represents Box Jenkins models and the non-linear component, which represents one of the methods of artificial intelligence, which is long short term memory (LSTM) and the gated recurrent unit (GRU) which represents deep learning models. It was found that the proposed hybrid models in the prediction process when conducting simulation for different sample sizes and when applied to the daily crude oil price time series data, were more efficient than the single models, and the comparison between the single models and the proposed hybrid models was made by comparison scale, mean square error (MSE), and the results showed that the proposed hybrid models have the ability to predict crude oil prices, as they gave more accurate and efficient results. | ||
| کلیدواژهها | ||
| ARIMA؛ Long Short Term Memory؛ Time Series Forecasting؛ Gated Recurrent Unit؛ Hybrid Model | ||
|
آمار تعداد مشاهده مقاله: 17,827 تعداد دریافت فایل اصل مقاله: 12,673 |
||