Next Article in Journal
On the Form and Growth of Complex Crystals: The Case of Tsai-Type Clusters
Next Article in Special Issue
Scale Effect and Anisotropy Analyzed for Neutrosophic Numbers of Rock Joint Roughness Coefficient Based on Neutrosophic Statistics
Previous Article in Journal
Spatio-Temporal Symmetry—Point Groups with Time Translations
Previous Article in Special Issue
An Efficient Image Segmentation Algorithm Using Neutrosophic Graph Cut
Open AccessArticle

Forecasting Model Based on Neutrosophic Logical Relationship and Jaccard Similarity

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.
Symmetry 2017, 9(9), 191;
Received: 7 August 2017 / Revised: 7 September 2017 / Accepted: 8 September 2017 / Published: 11 September 2017
(This article belongs to the Special Issue Neutrosophic Theories Applied in Engineering)
The daily fluctuation trends of a stock market are illustrated by three statuses: up, equal, and down. These can be represented by a neutrosophic set which consists of three functions—truth-membership, indeterminacy-membership, and falsity-membership. In this paper, we propose a novel forecasting model based on neutrosophic set theory and the fuzzy logical relationships between the status of historical and current values. Firstly, the original time series of the stock market is converted to a fluctuation time series by comparing each piece of data with that of the previous day. The fluctuation time series is then fuzzified into a fuzzy-fluctuation time series in terms of the pre-defined up, equal, and down intervals. Next, the fuzzy logical relationships can be expressed by two neutrosophic sets according to the probabilities of different statuses for each current value and a certain range of corresponding histories. Finally, based on the neutrosophic logical relationships and the status of history, a Jaccard similarity measure is employed to find the most proper logical rule to forecast its future. The authentic Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) time series datasets are used as an example to illustrate the forecasting procedure and performance comparisons. The experimental results show that the proposed method can successfully forecast the stock market and other similar kinds of time series. We also apply the proposed method to forecast the Shanghai Stock Exchange Composite Index (SHSECI) to verify its effectiveness and universality. View Full-Text
Keywords: fuzzy time series; forecasting; fuzzy logical relationship; neutrosophic set; Jaccard similarity fuzzy time series; forecasting; fuzzy logical relationship; neutrosophic set; Jaccard similarity
Show Figures

Figure 1

MDPI and ACS Style

Guan, H.; Guan, S.; Zhao, A. Forecasting Model Based on Neutrosophic Logical Relationship and Jaccard Similarity. Symmetry 2017, 9, 191.

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.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop