Nickel Price Forecast Based on the LSTM Neural Network Optimized by the Improved PSO Algorithm
المؤلفون المشاركون
Shao, Bilin
Bian, Genqing
Li, Maolin
Zhao, Yu
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-15، 15ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-09-08
دولة النشر
مصر
عدد الصفحات
15
التخصصات الرئيسية
الملخص EN
Nickel is a vital strategic metal resource with commodity and financial attributes simultaneously, whose price fluctuation will affect the decision-making of stakeholders.
Therefore, an effective trend forecast of nickel price is of great reference for the risk management of the nickel market’s participants; yet, traditional forecast methods are defective in prediction accuracy and applicability.
Therefore, a prediction model of nickel metal price is proposed based on improved particle swarm optimization algorithm (PSO) combined with long-short-term memory (LSTM) neural networks, for higher reliability.
This article introduces a nonlinear decreasing assignment method and sine function to improve the inertia weight and learning factor of PSO, respectively, and then uses the improved PSO algorithm to optimize the parameters of LSTM.
Nickel metal’s closing prices in London Metal Exchange are sampled for empirical analysis, and the improved PSO-LSTM model is compared with the conventional LSTM and the integrated moving average autoregressive model (ARIMA).
The results show that compared with the standard PSO, the improved PSO has a faster convergence rate and can improve the prediction accuracy of the LSTM model effectively.
In addition, compared with the conventional LSTM model and the integrated moving average autoregressive (ARIMA) model, the prediction error of the LSTM model optimized by the improved PSO is reduced by 9% and 13%, respectively, which has high reliability and can provide valuable guidance for relevant managers.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Shao, Bilin& Li, Maolin& Zhao, Yu& Bian, Genqing. 2019. Nickel Price Forecast Based on the LSTM Neural Network Optimized by the Improved PSO Algorithm. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-15.
https://search.emarefa.net/detail/BIM-1194566
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Shao, Bilin…[et al.]. Nickel Price Forecast Based on the LSTM Neural Network Optimized by the Improved PSO Algorithm. Mathematical Problems in Engineering No. 2019 (2019), pp.1-15.
https://search.emarefa.net/detail/BIM-1194566
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Shao, Bilin& Li, Maolin& Zhao, Yu& Bian, Genqing. Nickel Price Forecast Based on the LSTM Neural Network Optimized by the Improved PSO Algorithm. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-15.
https://search.emarefa.net/detail/BIM-1194566
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1194566
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