scholarly journals Modeling the Context of the Problem Domain of Time Series with Type-2 Fuzzy Sets

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2947
Author(s):  
Anton A. Romanov ◽  
Aleksey A. Filippov ◽  
Valeria V. Voronina ◽  
Gleb Guskov ◽  
Nadezhda G. Yarushkina
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Uncertainty Modeling ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Time Series Modeling ◽  
Problem Domain ◽  
Contextual Modeling

Data analysis in the context of the features of the problem domain and the dynamics of processes are significant in various industries. Uncertainty modeling based on fuzzy logic allows building approximators for solving a large class of problems. In some cases, type-2 fuzzy sets in the model are used. The article describes constructing fuzzy time series models of the analyzed processes within the context of the problem domain. An algorithm for fuzzy modeling of the time series was developed. A new time series forecasting scheme is proposed. An illustrative example of the time series modeling is presented. The benefits of contextual modeling are demonstrated.

scholarly journals Type-2 Fuzzy Expert System Approach for Decision-Making of Financial Assets and Investing under Different Uncertainty

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Zuzana Janková ◽  
Petr Dostál
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Stock Market ◽  
Fuzzy Sets ◽  
Financial Time Series ◽  
Fuzzy Model ◽  
Exchange Traded Funds ◽  
International Equity Markets

Extensive research results of stock market time series using classical fuzzy sets (type-1) are available in the literature. However, type-1 fuzzy sets cannot fully capture the uncertainty associated with stock market developments due to their limited descriptiveness. This paper fills a scientific gap and focuses on type-2 fuzzy logic applied to stock markets. Type-2 fuzzy sets may include additional uncertainty resulting from unclear, uncertain, or inaccurate financial data through which model inputs are calculated. Here we propose four methods based on type-2 fuzzy logic, which differ in the level of uncertainty contained in fuzzy sets and compared with the type-1 fuzzy model. The case study aims to create a model to support investment decisions in Exchange-Traded Funds (ETFs) listed on international equity markets. The created models of type-2 fuzzy logic are compared with the classic type-1 fuzzy logic model. Based on the results of the comparison, it can be said that type-2 fuzzy logic with dual fuzzy sets is able to better describe data from financial time series and provides more accurate outputs. The results reflect the capability and effectiveness of the approach proposed in this document. However, the performance of type-2 fuzzy logic models decreases with the inclusion of increasing uncertainty in fuzzy sets. For further research, it would be appropriate to examine the different levels of uncertainty in the input parameters themselves and monitor the performance of such a modified model.

scholarly journals An Interval Type-2 Fuzzy Logic System for Stock Index Forecasting Based on Fuzzy Time Series and a Fuzzy Logical Relationship Map

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 69107-69119 ◽  
Author(s):  
Joe-Air Jiang ◽  
Chih-Hao Syue ◽  
Chien-Hao Wang ◽  
Jen-Cheng Wang ◽  
Jiann-Shing Shieh
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Fuzzy Logic System ◽  
Fuzzy Time Series ◽  
Stock Index ◽  
Logical Relationship ◽  
Interval Type ◽  
Stock Index Forecasting ◽  
Logic System

Penerapan Logika Samar dalam Peramalan Data Runtun Waktu

2011 ◽  
Vol 3 (2) ◽  
pp. 11-15
Author(s):  
Seng Hansun
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Time Series Analysis ◽  
Fuzzy Sets ◽  
Fuzzy System ◽  
Time Series Data ◽  
Fuzzy Logic System ◽  
Series Data ◽  
Series Analysis ◽  
Logic System

Recently, there are so many soft computing methods been used in time series analysis. One of these methods is fuzzy logic system. In this paper, we will try to implement fuzzy logic system to predict a non-stationary time series data. The data we use here is Mackey-Glass chaotic time series. We also use MATLAB software to predict the time series data, which have been divided into four groups of input-output pairs. These groups then will be used as the input variables of the fuzzy logic system. There are two scenarios been used in this paper, first is by using seven fuzzy sets, and second is by using fifteen fuzzy sets. The result shows that the fuzzy system with fifteen fuzzy sets give a better forecasting result than the fuzzy system with seven fuzzy sets. Index Terms—forecasting, fuzzy logic, Mackey-Glass chaotic, MATLAB, time series analysis

A Novel Intuitionistic Fuzzy Time Series Method Based on Bootstrapped Combined Pi-Sigma Artificial Neural Network

2021 ◽  
Vol 01 ◽  
Author(s):  
Eren Bas ◽  
Erol Egrioglu ◽  
Emine Kölemen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Stock Exchange ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Intuitionistic Fuzzy ◽  
Forecasting Methods ◽  
Time Series Method ◽  
Artificial Neural

Background: Intuitionistic fuzzy time series forecasting methods have been started to solve the forecasting problems in the literature. Intuitionistic fuzzy time series methods use both membership and non-membership values as auxiliary variables in their models. Because intuitionistic fuzzy sets take into consideration the hesitation margin and so the intuitionistic fuzzy time series models use more information than fuzzy time series models. The background of this study is about intuitionistic fuzzy time series forecasting methods. Objective: The study aims to propose a novel intuitionistic fuzzy time series method. It is expected that the proposed method will produce better forecasts than some selected benchmarks. Method: The proposed method uses bootstrapped combined Pi-Sigma artificial neural network and intuitionistic fuzzy c-means. The combined Pi-Sigma artificial neural network is proposed to model the intuitionistic fuzzy relations. Results and Conclusion: The proposed method is applied to different sets of SP&500 stock exchange time series. The proposed method can provide more accurate forecasts than established benchmarks for the SP&500 stock exchange time series. The most important contribution of the proposed method is that it creates statistical inference: probabilistic forecasting, confidence intervals and the empirical distribution of the forecasts. Moreover, the proposed method is better than the selected benchmarks for the SP&500 data set.

Self-Organised Direction Aware Data Partitioning for Type-2 Fuzzy Time Series Prediction

2021 ◽  
Author(s):  
Arthur C. V. e Pinto ◽  
Petronio C. L. Silva ◽  
Frederico G. Guimaraes ◽  
Christian Wagner ◽  
Eduardo P. de Aguiar
Keyword(s):  
Time Series ◽  
Time Series Prediction ◽  
Data Partitioning ◽  
Fuzzy Time Series

Fuzzy Time Series Models for LNSZZS Forecasting

2012 ◽  
Vol 7 (19) ◽  
pp. 470-478 ◽  
Author(s):  
Sun Baiqing ◽  
Xiong Shan ◽  
Wu Berlin
Keyword(s):  
Time Series ◽  
Fuzzy Time Series ◽  
Time Series Models

Risk assessment for the industrial network based on interval type-2 fuzzy sets

Kybernetes ◽  
2019 ◽  
Vol 49 (3) ◽  
pp. 916-937
Author(s):  
Chao Ren ◽  
Xiaoxing Liu ◽  
Zongqing Zhang
Keyword(s):  
Risk Assessment ◽  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Risk Evaluation ◽  
Quantitative Risk Assessment ◽  
Content Type ◽  
Industrial Network ◽  
Risk Magnitude ◽  
Interval Type

Purpose The purpose of this paper is to develop a risk evaluation method for the industrial network under high uncertain environment. Design/methodology/approach This paper introduces an extended safety and critical effect analysis (SCEA) method, which takes the weight of each industry in a network into risk assessment. Furthermore, expert experience and fuzzy logic are introduced for the evaluation of other parameters. Findings The proposed approach not only develops weight as the fifth parameter in quantitative risk assessment but also applies the interval type-2 fuzzy sets to depict the uncertainty in the risk evaluation process. The risk rating of each parameter excluding weight is determined by using the interval type-2 fuzzy numbers. The risk magnitude of each industry in the network is quantified by the extended SCEA method. Research limitations/implications There is less study in quantitative risk assessment in the industrial network. Additionally, fuzzy logic and expert experience are expressed in the presented approach. Moreover, different parameters can be determined by different weights in network risk assessment in the future study. Originality/value The extended SCEA method presents a new way to measure risk magnitude for industrial networks. The industrial network is developed in risk quantification by assessing weights of nodes as a parameter into the extended SCEA. The interval type-2 fuzzy number is introduced to model the uncertainty of risk assessment and to express the risk evaluation information from experts.

scholarly journals High-Order Fuzzy Time Series Model Based on Generalized Fuzzy Logical Relationship

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Wangren Qiu ◽  
Xiaodong Liu ◽  
Hailin Li
Keyword(s):  
Time Series ◽  
Stock Exchange ◽  
Composite Index ◽  
High Order ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Logical Relationship ◽  
Proposed Model ◽  
The University ◽  
Fuzzy Logical

In view of techniques for constructing high-order fuzzy time series models, there are three methods which are based on advanced algorithms, computational methods, and grouping the fuzzy logical relationships, respectively. The last kind model has been widely applied and researched for the reason that it is easy to be understood by the decision makers. To improve the fuzzy time series forecasting model, this paper presents a novel high-order fuzzy time series models denoted asGTS(M,N)on the basis of generalized fuzzy logical relationships. Firstly, the paper introduces some concepts of the generalized fuzzy logical relationship and an operation for combining the generalized relationships. Then, the proposed model is implemented in forecasting enrollments of the University of Alabama. As an example of in-depth research, the proposed approach is also applied to forecast the close price of Shanghai Stock Exchange Composite Index. Finally, the effects of the number of orders and hierarchies of fuzzy logical relationships on the forecasting results are discussed.

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