Abstract
Making predictions according to historical values has long been regarded as common practice by many researchers. However, forecasting solely based on historical values could lead to inevitable over-complexity and uncertainty due to the uncertainties inside, and the random influence outside, of the data. Consequently, finding the inherent rules and patterns of a time series by eliminating disturbances without losing important details has long been a research hotspot. In this paper, we propose a novel forecasting model based on multi-valued neutrosophic sets to find fluctuation rules and patterns of a time series. The contributions of the proposed model are: (1) using a multi-valued neutrosophic set (MVNS) to describe the fluctuation patterns of a time series, the model could represent the fluctuation trend of up, equal, and down with degrees of truth, indeterminacy, and falsity which significantly preserve details of the historical values; (2) measuring the similarities of different fluctuation patterns by the Hamming distance could avoid the confusion caused by incomplete information from limited samples; and (3) introducing another related time series as a secondary factor to avoid warp and deviation in inferring inherent rules of historical values, which could lead to more comprehensive rules for further forecasting. To evaluate the performance of the model, we explored the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) as the major factor we forecast, and the Dow Jones Index as the secondary factor to facilitate the predicting of the TAIEX. To show the universality of the model, we applied the proposed model to forecast the Shanghai Stock Exchange Composite Index (SHSECI) as well.
Highlights
Financial forecasting problems are one of the most complex problems in the modern economic environment
The major contributions are: (1) using a multi-valued neutrosophic set (MVNS) to describe the fluctuation pattern of time series, the model could represent the fluctuation of up, equal, and down with degrees of truth, indeterminacy, and falsity introduced by the MVNS, which significantly preserved details of the historical values; (2) measuring the similarities of different fluctuation patterns of different time series by the Hamming distance could avoid the confusion caused by incomplete information from samples; and (3) due to the existence of likeness among similar types of historical values, in this model, we introduce another related time series as a secondary factor to avoid warp and deviation in inferring inherent rules of historical values, which could lead to more comprehensive rules for further forecasting
We propose a novel forecasting model for financial forecasting problems based on multi-valued neutrosophic logical relationships and Hamming distance
Summary
Financial forecasting problems are one of the most complex problems in the modern economic environment. These above extensions cannot properly solve the problems Under these circumstances, Wang [25] introduced multi-valued neutrosophic sets (MVNSs) to express the information and improve the operations and comparison methods of MVNSs. MVNSs are characterized by truth-membership, indeterminacy-membership, and false-membership functions that have a set of crisp values in the range [0, 1]. The remainder of this paper is organized as follows: we review and define some concepts of fuzzy-fluctuation time series and MVNSs. In the third section, a novel approach for forecasting is described based on MVNS theory and hamming distance.
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