Improving Deep Learning for Forecasting Accuracy in Financial Data
المؤلفون المشاركون
المصدر
Discrete Dynamics in Nature and Society
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-03-26
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
Financial forecasting is based on the use of past and present financial information to make the best prediction of the future financial situation, to avoid high-risk situations, and to increase benefits.
Such forecasts are of interest to anyone who wants to know the state of possible finances in the future, including investors and decision-makers.
However, the complex nature of financial data makes it difficult to get accurate forecasts.
Artificial intelligence, which has been shown to be suitable for analyzing very complex problems, can be applied to financial forecasting.
Financial data is both nonlinear and nonstationary, with broadband frequency features.
In other words, there is a large range of fluctuation, meaning that predictions made only using long short-term memory (LSTM) are not enough to ensure accuracy.
This study uses an LSTM model for analysis of financial data, followed by a comparison of the analytical results with the actual data to see which has a larger root-mean-square-error (RMSE).
The proposed method combines deep learning with empirical mode decomposition (EMD) to understand and predict financial trends from financial data.
The financial data for this study are from the Taiwan corporate social responsibility (CSR) index.
First, the EMD method is used to transform the CSR index data into a limited number of intrinsic mode functions (IMF).
The bandwidth of these IMFs becomes narrower, with regular cyclic, periodic, or seasonal components in the time domain.
In other words, the range of fluctuation is small.
LSTM is a good way to forecast cyclic or seasonal data.
The forecast result is obtained by adding all the IMFs together.
It has been verified in past studies that only the LSTM and LSTM combined with the EMD can be used.
The analytical results show that smaller RMSEs can be obtained using the LSTM combined with EMD compared to real data.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Lin, Shih-Lin& Huang, Hua-Wei. 2020. Improving Deep Learning for Forecasting Accuracy in Financial Data. Discrete Dynamics in Nature and Society،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1153196
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Lin, Shih-Lin& Huang, Hua-Wei. Improving Deep Learning for Forecasting Accuracy in Financial Data. Discrete Dynamics in Nature and Society No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1153196
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Lin, Shih-Lin& Huang, Hua-Wei. Improving Deep Learning for Forecasting Accuracy in Financial Data. Discrete Dynamics in Nature and Society. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1153196
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1153196
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