Fuzzy Logical Relationships Research Articles

A novel high order hesitant fuzzy time series forecasting by using mean aggregated membership value with support vector machine

Over the last three decades, several researchers have been putting their efforts into developing non-deterministic fuzzy time series (FTS) models using the traditional fuzzy set. However, considering a set of membership values to each element of the time series, the hesitant fuzzy set glorifies the chances to capture the fuzziness and uncertainty due to randomness better than the traditional fuzzy set. Motivated by this, the present study proposes a novel hesitant FTS forecasting model employing a support vector machine (HFTSF-SVM). In this model, to diminish the computational complexity and increase the forecasting accuracy, we have addressed three key issues by proposing three new methods such as (a) a length-based discretization technique is developed to compute the number of intervals, (b) two sets of universe of discourse are determined for the same time series to compute the non-membership and membership value for each observation (c) each observation along with its mean aggregated membership values are adopted to construct the fuzzy logical relationships (FLR). To assess the competency and reliability of the proposed HFTSF-SVM model, six promising FTS models from the recent literature and four promising time series forecasting (TSF) models are considered for comparison using sixteen time series datasets. Extensive statistical analyses of obtained results confirm the supremacy of the proposed HFTSF-SVM model.

A HYBRID PSO-SA SCHEME FOR IMPROVING ACCURACY OF FUZZY TIME SERIES FORECASTING MODELS

Forecasting methods based on fuzzy time series have been examined intensively during last years. Three main factors which affect the accuracy of those forecasting methods are length of intervals, the way of establishing fuzzy logical relationship groups, and defuzzification techniques. Many researches focus on optimizing length of intervals in order to improve forecasting accuracies by utilizing various optimization techniques. In the line of that research trend, in this paper, a hybrid particle swarm optimization combined with simulated annealing (PSO-SA) algorithm is proposed to optimize length of intervals to improve forecasting accuracies. The experimental results in comparison with the existing forecasting models show that the proposed forecasting model is an effective forecasting model.

Optimization of Markov Weighted Fuzzy Time Series Forecasting Using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)

The Markov Weighted Fuzzy Time Series (MWFTS) is a method for making predictions based on developing a fuzzy time series (FTS) algorithm. The MWTS has overcome certain limitations of FTS, such as repetition of fuzzy logic relationships and weight considerations of fuzzy logic relationships. The main challenge of the MWFTS method is the absence of standardized rules for determining partition intervals. This study compares the MWFTS model to the partition methods Genetic Algorithm-Fuzzy K-Medoids clustering (GA-FKM) and Fuzzy K-Medoids clustering-Particle Swarm Optimization (FKM-PSO) to solve the problem of determining the partition interval and develop an algorithm. Optimal partition optimization. The GA optimization algorithm’s performance on GA-FKM depends on optimizing the clustering of FKM to obtain the most significant partition interval. Implementing the PSO optimization algorithm on FKM-PSO involves maximizing the interval length following the FKM procedure. The proposed method was applied to Anand Vihar, India’s air quality data. The MWFTS method combined with the GA-FKM partitioning method reduced the mean absolute square error (MAPE) from 17.440 to 16.85%. While the results of forecasting using the MWFTS method in conjunction with the FKM-PSO partition method were able to reduce the MAPE percentage from 9.78% to 7.58%, the MAPE percentage was still 9.78%. Initially, the root mean square error (RMSE) score for the GA-FKM partitioning technique was 48,179 to 47,01. After applying the FKM-PSO method, the initial RMSE score of 30,638 was reduced to 24,863. Doi: 10.28991/ESJ-2022-06-06-010 Full Text: PDF

APPLICATION OF THE FUZZY TIME SERIES MODEL IN CLOTHING MATERIAL STOCK FORECASTING

The application of fuzzy time series is used to view the stock of clothing materials. As for the problem so far, CV Duta Express does not have a model for seeing the stock of complete materials in the warehouse, so the process is not optimal. This will have an impact on orders that come in at the same time and in large quantities. to avoid stock shortages, which resulted in the company experiencing losses. The purpose of this study is to make it easier to predict the stock of clothing materials and to be able to analyze every stock management at CV Duta Express with a fuzzy time series model. The variables used are stock needs, the amount of stock of school clothes, batik clothes, and pants. The research methodology in data collection consists of product type data from 2018–2021 at CV Duta Express Aceh Utara. Data analysis needs consist of school clothes, school pants, and clothes. Next, the fuzzy time series process determines the actual data is the type of school clothes, and what is forecast is sales for January 2018–2021 at the end of December. For a value of 1108 A2 fuzzification value, 172 A4 fuzzification value for batik clothes, and 894 pants with an A1 fuzzification value, then the value of the universe set used is U = [26, 323]. The value of forming a linguistic set is based on the length of the interval U3 = [111,153], U4 = [153,196], and U5 = [196,238]. The result of the fuzzification value from historical data for the value of 172 fuzzification is A4, for data of 133 fuzzification is A3. The formation of Fuzzy Logic Relationship (FLR) values for the period 1/7/2021 to 1/13/2021 is obtained from the data range A4=174 and range A3=125 in each period to be related. The results of forecasting with fuzzy time series testing at the end of December 2021 are 196 stocks of clothes that must be optimized in the following month. The test results in this study are to see if the error value using the AFER model is 0.4511% while the RMSE test value has an error value of 5.0199. After being calculated for the forecast every month, the average obtained for AFER is 0.50154 % and the RMSE is 9.86518.. Keywords : Forecasting, Fuzzy Time Series, stock

Quantity Based weights forecasting for TAIEX

Many models of the forecasting have been proposed on stock index. In this paper, the consideration of Taiwan Stock Exchange Capitalization Weights Stock Index (TAIEX) has been taken and founded forecasted index value. Here forecasting is done by using Quantity based fuzzy time series (QBFTS), Quantity based factor TAIEX and forecasting methods are QBFLR and QBFLRGs. The approach of Statistical weights assigned for different weights to various quantity based fuzzy logical relationships for considered quantity-based dataset of TAIEX. Hence got the comparative result better than the other models.

Incorporate long association into high-order fuzzy logical relationship based time series forecasting

In fuzzy logical relationship (FLR) based forecasting models, FLRs play a vital role. In each FLR of such models, the engaged observations in premise and consequent are consecutive at time. The association (named short association in this paper) implied by the premise and consequent of such an FLR can reflect some of the properties and regularities hidden in a time series, which has been verified by more successful applications. However, there exists other kind of associations that cannot be described by short association. In this paper, the concept of long association to describe these different associations is proposed. And long-association FLRs are then proposed to reflect such long associations. For a long-association FLR, the engaged observations in the premise and consequent are not consecutive at time. This new kind of FLRs exists more and can overcome one deficiency of the existing FLR based forecasting models: no FLR available for forecasting occurred often at some prediction moments. Departure from long-association FLRs, we construct trend long-association FLRs to reflect the trend long associations in time series and design a novel forecasting model. The advantages of the proposed (trend) long-association FLRs and the superiority of the proposed model are verified in experiments with comparisons with other forecasting models.

Fuzzy Time Series Forecasting Approach using LSTM Model

In the present scenario, fuzzy time series forecasting (FTSF) is an interesting concept by the researchers to approach the uncertainty in the dataset. In the current study, we proposed a fuzzy long short term memory (FLSTM) model to forecast a wide range of time series (TS) dataset with less computational complexity. The present research mainly focuses on two issues such as 1) in order to obtain the number of intervals (NOIs) of the universe of discourse (UOD) the trend based discretization (TBD) approach is applied, and 2) the subscript of the fuzzy set associated with the crisp observation is considered to establish the fuzzy logical relationships (FLRs) for the proposed FLSTM model. To demonstrate the forecasting ability of the FLSTM model, six TS datasets with three profound FTSF models are considered. The empirical result analysis revealed that, in all measured the proposed model outperformed and showed better result than its alternatives. The outcome of the different FTSF models on different measures proves the outperformance of the FLSTM model than its competitors.

Forecasting of The Crime Rate Using Automatic Clustering and Fuzzy Logic Relationship Method In North Sumatra

Currently the crime rate is very alarming and reported in various mass and electronic media. The high crime rate in the Province of Nort Sumatra is very unsettling for the community. The purpose of this research is to get the result of forecasting the crime ratel in the 2021 - 2024 using Automatic Clustering And Fuzzy Logic Relationship (ACFLR) method. The advantage of this method is that the method has a high level of accuracy because the Mean Absolute Percentage Error (MAPE) value is relative small and the results of forecasting analysis obtained in 2021 there are 31522 cases, in 2022 are 31533 cases, in 2023 are 31574 cases and the last one in 2024 was 31602 cases. In addition, the prediction error rate MAPE obtained is 0,35 %
 
 Keywords: Crime rate, Automatic Clustering And Fuzzy Logic Relationship.

Implement multi-step-ahead forecasting with multi-point association fuzzy logical relationship for time series

In the existing high-order fuzzy logical relationship (FLR) based forecasting model, each FLR is used to describe the association between multiple premise observations and a consequent observation. Therefore, these FLRs concentrate on the one-step-ahead forecasting. In real applications, there exist another kind of association: the association between multiple premise observations and multiple consequent observations. For such association, the existing FLRs can’t express and ignored. To depict it, the high-order multi-point association FLR is raised in this study. The antecedent and consequent of a high-order multi-point association FLR are consisted of multiple observations. Thus, the proposed FLR reflects the influence of multiple premise observations on the multiple consequent observations, and can be applied for multi-step-ahead forecasting with no cumulative errors. On the basis of high-order multi-point association FLR, the high-order multi-point trend association FLR is constructed, it describes the trend association in time series. By using these two new kinds of FLRs, a fuzzy time series based multi-step-ahead forecasting model is established. In this model, the multi-point (trend) association FLRs effective in capturing the associations of time series and improving forecasting accuracy. The benefits of the proposed FLRs and the superior performance of the established forecasting model are demonstrated through the experimental analysis.

FORECASTING JUMLAH MAHASISWA BARU MENGGUNAKAN METODE AUTOMATIC CLUSTERING AND FUZZY LOGIC RELATIONSHIP MARKOV CHAIN (STUDI KASUS : FAKULTAS TEKNOLOGI INFORMASI DAN KOMUNIKASI VISUAL INSTITUT SAINS DAN TEKNOLOGI AL-KAMAL)


 Abstraction
 Forecasting or forecasting is a calculation analysis technique that is done by carrying out qualitative and quantitative approaches to think about future events using reference data in the past. The purpose of this study is to predict the number of new students at the Faculty of Information and Visual Communication Technology at the Al-Kamal Institute of Science and Technology in the academic year 2020/2021. Prediction of the number of new students in the Faculty of Information and Visual Communication Technology of the Al-Kamal Institute of Science in the future accurately is very important to do, because many decisions can be taken by the Leaders of the Al-Kamal Institute of Science and Technology from these predictions. Markov Chain Automatic Clustering and Fuzzy Logic Relationship Method was chosen because it has a better level of accuracy among other Fuzzy Logic methods. The data used in this study are secondary data obtained from the Academic Information System of the Al-Kamal Institute of Science and Technology. Based on this research it was found that the predicted results of the number of new students of the Faculty of Information and Visual Communication Technology at the Al-Kamal Institute of Science and Technology in the academic year 2020/2021 amounted to 64 with a MAPE of 8.25%

Fuzzy time series based on frequency density-based partitioning and k-means clustering for forecasting exchange rate

Fuzzy Time Series (FTS) has been growing rapidly in recent years. There are many models that were developed. In this paper, we propose a new method to forecast exchange rate data by combining some models. Firstly, we use the average-based interval to make optimal interval numbers. Secondly, we use frequency density-based partitioning for optimal partitioning. In this part, we divide the three highest frequency of intervals into four, three, and two sub-intervals, respectively, and discarding intervals if there is no data distributed. And thirdly, we use k-means clustering to construct the Fuzzy Logical Relationship Group (FLRG). We divide Fuzzy Logical Relationship (FLR) into 16 initial clusters. Then we evaluate model by calculating the error value using MSE (Mean Squared Error) and AFER (Average Forecasting Error Rates). The study case of this paper is daily exchange rate data (USD to IDR) started from January until May with its unstable fluctuation caused by Pandemic Covid-19. The study aims to obtain a forecasting model of exchange rate data as the preparation and evaluation for future conditions.

Recognition and determination of fuzzy logical relationship in the system fault evolution process

To study the fuzzy logical relationship between the cause event and result event in the system fault evolution process, a method of forming the expression of the fuzzy logical relationship by superposition of the basic logical relationships and fuzzy membership degree is proposed. It is called the fuzzy logical structure function. Two problems need to be solved to determine the fuzzy logical structure function. One is to determine the basic logical relationships; the other is to determine the fuzzy membership degree. The authors transform the 14 kinds of flexible logical relationships into the event occurrence logical relationship formula, which can be regarded as the basic logical relationships; the latter uses the enumeration method to change the fuzzy membership degree and obtains the optimal fuzzy membership degree when the fitness function is closest to 0. Finally, the form of the fuzzy logical structure function is obtained. The fuzzy logical relationship between the cause event and the result event is determined by using a neural network. With an example, it can be seen that the logical relationships between the cause event and the result event are implication and average, and its fuzzy membership degree is more than 80%; when using a neural network, the fuzzy membership degree has no practical significance, but it can obtain the result event probability more accurately through the cause event probability. Finally, the advantages and disadvantages of the method are summarised.

Integrate new cross association fuzzy logical relationships to multi-factor high-order forecasting model of time series

In any multi-factor high-order fuzzy logical relationship (FLR) based forecasting model, a FLR reflects the influence of both the main factor (the forecasted factor) and all the influence factors on the main factor. Thus, the antecedent of a FLR includes multiple premises related to the main factor as well as all the influence factors. In real time series, there may exist another kind of influence: the cross association influence which is from a part of influence factor(s) on the main factor. To describe such kind of influence, we propose the concept of multi-factor high-order cross association FLRs (CAFLRs). The antecedent of a CAFLR includes some premises related to a part of influence factors. The proposed CAFLRs are divided into two categories: short-cross association FLRs and long-cross association FLRs, which describe the influence on the consequent observation from the premise observations at the closest consecutive moments and the premise observations at the non-closest non-consecutive moments respectively. Based on the concept of CAFLRs, a novel forecasting model is built up. In the proposed model, more FLRs than in the existing models can be mined from historical observations and added to the rule base, which further improve the prediction accuracy by raising the possibility of finding available forecasting FLRs. Superior performance of the proposed model has been verified in the experiments by comparing with Nonlinear Autoregressive Neural Networks, Autoregressive Model, Support Vector Regression and some other FLR based forecasting models.

A TIME SERIES FORECASTING MODEL BASED ON LINGUISTIC FORECASTING RULES

The fuzzy time series (FTS) forecasting models have been being studied intensively over the past few years. Most of the researches focus on improving the effectiveness of the FTS forecasting models using time-invariant fuzzy logical relationship groups proposed by Chen et al. In contrast to Chen’s model, a fuzzy set can be repeated in the right-hand side of the fuzzy logical relationship groups of Yu’s model. N. C. Dieu enhanced Yu’s forecasting model by using the time-variant fuzzy logical relationship groups instead of the time-invariant ones. The forecasting models mentioned above partition the historical data into subintervals and assign the fuzzy sets to them by the human expert’s experience. N. D. Hieu et al. proposed a linguistic time series by utilizing the hedge algebras quantification to converse the numerical time series data to the linguistic time series. Similar to the FTS forecasting model, the obtained linguistic time series can define the linguistic, logical relationships which are used to establish the linguistic, logical relationship groups and form a linguistic forecasting model. In this paper, we propose a linguistic time series forecasting model based on the linguistic forecasting rules induced from the linguistic, logical relationships instead of the linguistic, logical relationship groups proposed by N. D. Hieu. The experimental studies using the historical data of the enrollments of University of Alabama observed from 1971 to 1992 and the daily average temperature data observed from June 1996 to September 1996 in Taipei show the outperformance of the proposed forecasting models over the counterpart ones.

Fuzzy Time Series Forecasting Model Based on Intuitionistic Fuzzy Sets via Delegation of Hesitancy Degree to the Major Grade De-i-fuzzification Method

Fuzzy time series is a powerful tool to forecast the time series data under uncertainty. Fuzzy time series was first initiated with fuzzy sets and then generalized by intuitionistic fuzzy sets. The intuitionistic fuzzy sets consider the degree of hesitation in which the degree of non-membership is incorporated. In this paper, a fuzzy set time series forecasting model based on intuitionistic fuzzy sets via delegation of hesitancy degree to the major grade de-i-fuzzification approach was developed. The proposed model was implemented on the data of student enrollments at the University of Alabama. The forecasted output was obtained using the fuzzy logical relationships of the output, and the performance of the forecasted output was compared with the fuzzy time series forecasting model based on fuzzy sets using the mean square error, root mean square error, mean absolute error, and mean absolute percentage error. The results showed that the forecasting model based on induced fuzzy sets from intuitionistic fuzzy sets performs better compared to the fuzzy time series forecasting model based on fuzzy sets.

A novel probabilistic intuitionistic fuzzy set based model for high order fuzzy time series forecasting

The present research proposes a novel probabilistic intuitionistic fuzzy time series forecasting (PIFTSF) model using support vector machine (SVM) to address both uncertainty and non-determinism associated with real world time series data. In this model, the probability of membership values of crisp observation is determined to handle the statistical uncertainty. At the same time, the intuitionistic fuzzy element of crisp observation is determined to handle the non-statistical uncertainty along with non-determinism. Then, both the membership values are aggregated to obtain the probabilistic intuitionistic fuzzy element which handles both statistical and non-statistical uncertainty along with non-determinism due to hesitancy. Additionally, a novel trend-based discretization (TBD) method is proposed to determine the universe of discourse and number of intervals (NOIs) of fuzzy time series (FTS). For the first time, the fuzzy logical relationships (FLRs) are established for the probabilistic intuitionistic fuzzy set by considering the ratio trend variation (RTV) of crisp observation along with the mean of aggregated membership values which is modelled using SVM. The efficiency of the proposed PIFTSF model is demonstrated with sixteen diversified time series datasets and seven existing FTS models. A sensitivity analysis is carried out with respect to different design strategies to ensure the robustness of the proposed model. Extensive statistical analyses on obtained results confirm the superiority of the proposed model over other existing models. Further, Wilcoxon signed rank test, and Friedman and Nemenyi hypothesis test ensures the accuracy, robustness and reliability of the proposed model against its counterparts.

Sparepart sales clusterization and prediction using automatic clustering algorithm

Creating an information system for a store becomes an important part in a business process as an effort to facilitate the running of the business process. Therefore, the existence of this information system will make it easier for the store to get information on products, recording the number of products entering and leaving, supplier information and can predict the number of sales in the coming month. Prediction of sales in the coming month is very important to facilitate the store in preparing stock of product in the coming month. The design of this program applies the automatic clustering method and fuzzy logical relationship that manages the historical data of the number of sales each month in the previous period, so we get a prediction of the number of sales in the coming month, which is useful for the store in preparing the amount of stock in the coming months. Based on the results of tests conducted, this program can provide predictions of the number of sales in the coming months. Tests are carried out on two product samples, where the products tested have an accuracy level of 86.20% and 90%, respectively.

Stock index forecasting: A new fuzzy time series forecasting method

AbstractThis paper presents a new fuzzy time series forecasting model based on technical analysis, affinity propagation (AP) clustering, and a support vector regression (SVR) model. Technical analysis indicators are divided into three categories to construct multivariate fuzzy logical relationships. AP clustering without specifying the number of clusters is used to obtain a suitable partition for the universe of discourse, and the representative exemplars are generated as defuzzied values. The SVR model is employed to explore the unrecognized relationships and modify the forecasts. In addition, the error‐based evaluation criteria are applied to evaluate the methods. The performance of the method is evaluated using the Taiwan Capitalization Weighted Stock Index (TAIEX), Standard & Poor's 500 Index (S&P500), and Dow Jones Industrial Average (DJIA) dataset, and the experimental results demonstrate that the proposed method outperforms some classic models.

Strong α-cut and associated membership-based modeling for fuzzy time series forecasting

In this paper, a method is proposed to deal with factors affecting the fuzzy time series forecasting. A new fuzzification process is used by considering all the fuzzy sets with nonzero membership values corresponding to the data points. A strong alpha-cut based method is presented to select appropriate fuzzy logical relationships that carry importance in analyzing the trend of time series. Further, a unique defuzzification approach based on weights is proposed to get crisp variation. This obtained variation is finally converted to the forecasted value. The presented method is tested on the benchmark enrolment dataset of Alabama University and seven datasets of the Taiwan Capitalization Weighted Stock Index. On comparing the results, it is observed that the presented method performs better than the existing methods. Also, the statistical measures indicate the good forecasting results of the presented method.

A Hybrid Fuzzy Time Series Technique for Forecasting Univariate Data

In this paper a hybrid forecasting technique that integrates Cat Swarm optimization Clustering (CSO-C) and Particle Swarm Optimization (PSO) with Fuzzy Time Series (FTS) forecasting is presented. In the three stages of FTS, CSO-C found application at the fuzzification module where its efficient capability in terms of data classification was utilized to neutrally divide the universe of discourse into unequal parts. Then, disambiguated fuzzy relationships were obtained using Fuzzy Set Group (FSG). In the final stage, PSO was adopted for optimization; by tuning weights assigned to fuzzy sets in a rule. This rule is a fuzzy logical relationship induced from FSG. The forecasting results showed that the proposed method outperformed other existing methods; using RMSE and MAPE as performance metrics.