Next Article in Journal
A New Chaotic System with Stable Equilibrium: Entropy Analysis, Parameter Estimation, and Circuit Design
Previous Article in Journal
Transpiration and Viscous Dissipation Effects on Entropy Generation in Hybrid Nanofluid Flow over a Nonlinear Radially Stretching Disk
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Entropy 2018, 20(9), 669;

A Forecasting Model Based on High-Order Fluctuation Trends and Information Entropy

School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan 250014, China
Rensselaer Polytechnic Institute, Troy, NY 12180, USA
School of Management, Jiangsu University, Zhenjiang 212013, China
Author to whom correspondence should be addressed.
Received: 31 July 2018 / Revised: 22 August 2018 / Accepted: 3 September 2018 / Published: 4 September 2018
(This article belongs to the Section Information Theory, Probability and Statistics)
Full-Text   |   PDF [3666 KB, uploaded 4 September 2018]   |  


Most existing high-order prediction models abstract logical rules that are based on historical discrete states without considering historical inconsistency and fluctuation trends. In fact, these two characteristics are important for describing historical fluctuations. This paper proposes a model based on logical rules abstracted from historical dynamic fluctuation trends and the corresponding inconsistencies. In the logical rule training stage, the dynamic trend states of up and down are mapped to the two dimensions of truth-membership and false-membership of neutrosophic sets, respectively. Meanwhile, information entropy is employed to quantify the inconsistency of a period of history, which is mapped to the indeterminercy-membership of the neutrosophic sets. In the forecasting stage, the similarities among the neutrosophic sets are employed to locate the most similar left side of the logical relationship. Therefore, the two characteristics of the fluctuation trends and inconsistency assist with the future forecasting. The proposed model extends existing high-order fuzzy logical relationships (FLRs) to neutrosophic logical relationships (NLRs). When compared with traditional discrete high-order FLRs, the proposed NLRs have higher generality and handle the problem caused by the lack of rules. The proposed method is then implemented to forecast Taiwan Stock Exchange Capitalization Weighted Stock Index and Heng Seng Index. The experimental conclusions indicate that the model has stable prediction ability for different data sets. Simultaneously, comparing the prediction error with other approaches also proves that the model has outstanding prediction accuracy and universality. View Full-Text
Keywords: high-order fluctuation trends; forecasting; information entropy; neutrosophic sets high-order fluctuation trends; forecasting; information entropy; neutrosophic sets

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Guan, H.; Dai, Z.; Guan, S.; Zhao, A. A Forecasting Model Based on High-Order Fluctuation Trends and Information Entropy. Entropy 2018, 20, 669.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top