Open AccessA Two-Factor Fuzzy-Fluctuation Time Series Forecasting Model for Stock Markets Based on a Probabilistic Linguistic Preference Relationship and Similarity Measure
Author(s) -
Aiwu Zhao,
Junhong Gao,
Hongjun Guan
Publication year - 2021
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2021.3122142
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
An increasing number of scholars have tried to incorporate external factors affecting the disturbance of a time series into their forecasting models. However, these studies only verify the linkage relationship of two or more time series by empirical tests without providing any theoretical explanation. This makes it difficult to choose a linkage time series without using many tests. In this paper, a novel two-factor fuzzy-fluctuation time series (FFTS) forecasting model is proposed based on the probabilistic linguistic preference relationship (PLPR) and similarity measure. It not only proposes the idea of combining external factors with internal potential trends but also explains the linkage mechanism of time series fluctuations from the perspective of behavioral preference. Specifically, the probabilistic linguistic preference logical relationship (PLPLR) is employed to express the fluctuation behavior rule and preference attribute from the history testing dataset. The Euclidean distance or Hamming distance between the “current state” and the left side of training PLPLRs is introduced as a similarity comparison method for the identification of appropriate rules. The proposed model is tested using a traditional time series (e.g., the enrollment of the University of Alabama) to compare its performance with existing models. The model is also employed to forecast realistic stock markets, such as the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and Hang Seng Index (HSI). The performance comparison illustrates the effectiveness and universality of the model.