Stock index forecasting: A new fuzzy time series forecasting method

Wu, Hao; Huang, Long; Wang, Yue; Wang, Yanqi

doi:10.1002/for.2734

Cited by 18 publications

(6 citation statements)

References 47 publications

(54 reference statements)

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“…Wu et al [29] used multivariate fuzzy logic relationships based on a technical analysis, affinity propagation (AP), clustering, and a support vector regression (SVR) model to predict the performance of the Taiwan Capitalization Weighted Stock Index (TAIEX), the Standard & Poor's 500 (S&P500), and the Dow Jones Industrial Average (DJIA) dataset. Chourmouziadis et al [30] tested a model for the fuzzy prediction of the development of an investment portfolio on the Athens Stock Exchange, with the goal of outperforming the market (Buy and Hold strategy) in the medium and long terms.…”

Section: Related Workmentioning

confidence: 99%

A Fuzzy Multi-Criteria Evaluation System for Share Price Prediction: A Tesla Case Study

2023

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The article presents the predictive capabilities of a fuzzy multi-criteria evaluation system that operates on the basis of a non-fuzzy neural approach, but also one that is capable of implementing a learning paradigm and working with vague concepts. Within this context, the necessary elements of fuzzy logic are identified and the algebraic formulation of the fuzzy system is presented. It is with the help of the aforementioned that the task of predicting the short-term trend and price of the Tesla share is solved. The functioning of a fuzzy system and fuzzy neural network in the field of time series value prediction is discussed. The authors are inclined to the opinion that, despite the fact that a fuzzy neural network reacts in terms of applicability and effectiveness when solving prediction problems in relation to input data with a faster output than a fuzzy system, and is more “user friendly”, a sufficiently knowledgeable and experienced solver/expert could, by using a fuzzy system, achieve a higher speed of convergence in the learning process than a fuzzy neural network using the minimum range of input data carrying the necessary information. A fuzzy system could therefore be a possible alternative to a fuzzy neural network from the point of view of prediction.

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Section: Related Workmentioning

confidence: 99%

A Fuzzy Multi-Criteria Evaluation System for Share Price Prediction: A Tesla Case Study

2023

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“…e importance of attributes, as elaborated in the literature [6], was added to the granular computation of knowledge while used in solving the minimal attribute approximation, among others. In subsequent research, fuzzy quotient space theory was created by literature [7], improved by literature [8], perfected in the context of data mining, and so on. He Y [9] dealt with word computation and language dynamics and proposed a language dynamics system.…”

Section: Related Workmentioning

confidence: 99%

Support Vector Machine and Granular Computing Based Time Series Volatility Prediction

Yang

2022

Journal of Robotics

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With the development of information technology, a large amount of time-series data is generated and stored in the field of economic management, and the potential and valuable knowledge and information in the data can be mined to support management and decision-making activities by using data mining algorithms. In this paper, three different time-series information granulation methods are proposed for time-series information granulation from both time axis and theoretical domain: time-series time-axis information granulation method based on fluctuation point and time-series time-axis information granulation method based on cloud model and fuzzy time-series prediction method based on theoretical domain information granulation. At the same time, the granulation idea of grain computing is introduced into time-series analysis, and the original high-dimensional time series is granulated into low-dimensional grain time series by information granulation of time series, and the constructed information grains can portray and reflect the structural characteristics of the original time-series data, to realize efficient dimensionality reduction and lay the foundation for the subsequent data mining work. Finally, the grains of the decision tree are analyzed, and different support vector machine classifiers corresponding to each grain are designed to construct a global multiclassification model.

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“…In other studies, membership values were used to feed neural networks calculating fuzzy logic relationships (Yu and Huarng, 2010;Yolcu et al, 2016). Support vector regression was also used to compute unrecognized high-order fuzzy logic relationships from the stock market TS data (Wu et al, 2021). Alternatively, hybrid neural fuzzy models were used to learn the relationships, using an if-then rule-based inference mechanism to generate defuzzified forecasts.…”

Section: Construction Of Fuzzy Systemsmentioning

confidence: 99%

Neural intuitionistic fuzzy system with justified granularity

Hájek

Froelich

Olej

et al. 2022

Neural Comput & Applic

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Fuzzy systems are commonly used to model uncertainties occurring in data. They have been intensively investigated and extended to be used for the construction of time series forecasting models. In particular, intuitionistic fuzzy sets have been used to capture higher levels of uncertainty occurring in the modeled data. Neural networks were also used to reflect non-linearity relationships frequently observed in time series. Following this line of research, in this paper, we propose a new hybrid fuzzy system merging the most promising data representation methods with an advanced optimization technique, which is the principle of justified granularity. In this technique, we construct an innovative time series forecasting model. In the experimental part of the Article Title paper, we demonstrate the advantages arising from applying the proposed approach to metal price forecasting. We provide solid evidence that the proposed model is competitive with other state-of-the-art models known from the literature for forecasting horizons of one and five days.

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Stock index forecasting: A new fuzzy time series forecasting method

Cited by 18 publications

References 47 publications

A Fuzzy Multi-Criteria Evaluation System for Share Price Prediction: A Tesla Case Study

A Fuzzy Multi-Criteria Evaluation System for Share Price Prediction: A Tesla Case Study

Support Vector Machine and Granular Computing Based Time Series Volatility Prediction

Neural intuitionistic fuzzy system with justified granularity

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