A New Approach for Reconstruction of IMFs of Decomposition and Ensemble Model for Forecasting Crude Oil Prices
المؤلفون المشاركون
Aslam, Adnan
Xu, Peng
Aamir, Muhammad
Shabri, Ani
Ishaq, Muhammad
Li, Li
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-23، 23ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-10-06
دولة النشر
مصر
عدد الصفحات
23
التخصصات الرئيسية
الملخص EN
Accurate forecasting for the crude oil price is important for government agencies, investors, and researchers.
To cope with this issue, in this paper, a new paradigm is designed for the reconstruction of intrinsic mode functions (IMFs) of decomposition and ensemble models to reduce the complexity in computation and to enhance the forecasting accuracy.
Decomposition and ensemble methodologies significantly enhance the forecasting accuracy under the framework of “divide and conquer” with the proposed reconstruction of IMFs method.
The proposed approach used the autocorrelation at lag 1 of all IMFs for the reconstruction.
The ensemble empirical mode decomposition (EEMD) technique is employed to decompose the data into different IMFs.
Models that utilized the decomposed data relatively perform well, as compared to its application to the undecomposed data.
However, sometimes, the decomposition may produce poor results due to the error accumulation at the end.
Thus, in this study, the reconstruction of IMFs is proposed for minimizing the aforementioned error, thereby increasing the forecasting accuracy.
The Brent and West Texas Intermediate (WTI) datasets (daily and weekly) are exploited to compare the forecasting performance of autoregressive integrated moving average (ARIMA) along with artificial neural network (ANN) models with the decomposed data.
The results have proven that the new paradigm of reconstruction of IMFs through autocorrelation was a better and simple strategy that significantly improved the performance of single models including ARIMA and ANN.
Hence, it is concluded that the proposed model takes less computational time and achieved higher forecasting accuracy with the reconstruction of IMFs as opposed to using all IMFs.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Xu, Peng& Aamir, Muhammad& Shabri, Ani& Ishaq, Muhammad& Aslam, Adnan& Li, Li. 2020. A New Approach for Reconstruction of IMFs of Decomposition and Ensemble Model for Forecasting Crude Oil Prices. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-23.
https://search.emarefa.net/detail/BIM-1193135
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Xu, Peng…[et al.]. A New Approach for Reconstruction of IMFs of Decomposition and Ensemble Model for Forecasting Crude Oil Prices. Mathematical Problems in Engineering No. 2020 (2020), pp.1-23.
https://search.emarefa.net/detail/BIM-1193135
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Xu, Peng& Aamir, Muhammad& Shabri, Ani& Ishaq, Muhammad& Aslam, Adnan& Li, Li. A New Approach for Reconstruction of IMFs of Decomposition and Ensemble Model for Forecasting Crude Oil Prices. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-23.
https://search.emarefa.net/detail/BIM-1193135
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1193135
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر