A Hybrid Approach Integrating Multiple ICEEMDANs, WOA, and RVFL Networks for Economic and Financial Time Series Forecasting
المؤلفون المشاركون
Wu, Jiang
Zhou, Tengfei
Li, Taiyong
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-17، 17ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-10-22
دولة النشر
مصر
عدد الصفحات
17
التخصصات الرئيسية
الملخص EN
The fluctuations of economic and financial time series are influenced by various kinds of factors and usually demonstrate strong nonstationary and high complexity.
Therefore, accurately forecasting economic and financial time series is always a challenging research topic.
In this study, a novel multidecomposition and self-optimizing hybrid approach integrating multiple improved complete ensemble empirical mode decompositions with adaptive noise (ICEEMDANs), whale optimization algorithm (WOA), and random vector functional link (RVFL) neural networks, namely, MICEEMDAN-WOA-RVFL, is developed to predict economic and financial time series.
First, we employ ICEEMDAN with random parameters to separate the original time series into a group of comparatively simple subseries multiple times.
Second, we construct RVFL networks to individually forecast each subseries.
Considering the complex parameter settings of RVFL networks, we utilize WOA to search the optimal parameters for RVFL networks simultaneously.
Then, we aggregate the prediction results of individual decomposed subseries as the prediction results of each decomposition, respectively, and finally integrate these prediction results of all the decompositions as the final ensemble prediction results.
The proposed MICEEMDAN-WOA-RVFL remarkably outperforms the compared single and ensemble benchmark models in terms of forecasting accuracy and stability, as demonstrated by the experiments conducted using various economic and financial time series, including West Texas Intermediate (WTI) crude oil prices, US dollar/Euro foreign exchange rate (USD/EUR), US industrial production (IP), and Shanghai stock exchange composite index (SSEC).
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wu, Jiang& Zhou, Tengfei& Li, Taiyong. 2020. A Hybrid Approach Integrating Multiple ICEEMDANs, WOA, and RVFL Networks for Economic and Financial Time Series Forecasting. Complexity،Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1145515
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wu, Jiang…[et al.]. A Hybrid Approach Integrating Multiple ICEEMDANs, WOA, and RVFL Networks for Economic and Financial Time Series Forecasting. Complexity No. 2020 (2020), pp.1-17.
https://search.emarefa.net/detail/BIM-1145515
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wu, Jiang& Zhou, Tengfei& Li, Taiyong. A Hybrid Approach Integrating Multiple ICEEMDANs, WOA, and RVFL Networks for Economic and Financial Time Series Forecasting. Complexity. 2020. Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1145515
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1145515
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