A New Method for Short Multivariate Fuzzy Time Series Based on Genetic Algorithm and Fuzzy Clustering

Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks

This paper proposes a novel optimized model based on the combination of the fuzzy time series (FTS) method and Jacobi polynomials for predicting the behavior of fiber-reinforced polymer (FRP)-confined concrete under compression loads. In this model, the stress-strain curves are approximated by Jacobi polynomials. Then, the FTS method is used to develop a nonlinear relationship between the coefficients of Jacobi polynomials and the specimen features. Moreover, another FTS model is developed to predict the ultimate stress and the ultimate strain of FRP-confined concrete. A comparative analysis is performed to demonstrate the effectiveness of the proposed model. As compared with the well-known design-oriented models applied to 69 experiments, the root mean square error is decreased more than 50% by the proposed model. Also, the effects of input features on the physical behavior of the specimens as described by the proposed model are consistent with the relevant theories and experiments.

Partition-and-merge based fuzzy genetic clustering algorithm for categorical data

Fuzzy time series (FTS) is a method of making educated guesses by using fuzzy intervals, which correspond to time series clusters. It is also useful for data noise reduction and is based on rule-based forecasting. The aim of this article is to develop a vector autoregressive fuzzy integrated logical forecasting (VAR-FILF) model for time charter rates of Panamax and Handymax bulk carriers. Results are tested by using Chen's FTS method (cFTS), bivariate cFTS (Bi-cFTS) method and conventional time series methods, and the accuracy of the VAR-FILF method is found to be higher than these methods. In addition, the length of intervals affects the forecasting result and the accuracy of forecasting. Therefore, this study proposes the fuzzy C-means clustering method for the structuring of the fuzzy length of intervals of the FTS forecasting process.

In the era of uncertainty and chaos, the decision making is a complex process especially when it is related with the future prediction. The decision making process is fully dependent on the level of accuracy in forecasting. It is obvious that forecasting activities play a vital role in our daily life. The classical time series methods can not deal with forecasting problems in which, the values of time series are linguistic terms represented by fuzzy sets [1, 2]. Therefore, Song and Chissom [4] presented the theory of fuzzy time series to overcome the drawback of the classical time series methods. Time series prediction is a very important practical application with a diverse range of applications including economic and business planning, inventory and production control, weather forecasting, signal processing and control, Pattern matching etc. Based on the theory of fuzzy time series, Song presented some forecasting methods [4,5,6] to forecast the enrollments of the University of Alabama. Chen [8] presented a method to forecast the enrollments of the University of Alabama based on fuzzy time series. It has the advantage of reducing the calculation time and simplifying the calculation process. Hwang [10], used the differences of the enrollments to present a method to forecast the enrollments of the University of Alabama based on fuzzy time series. Huang extended Chen’s work presented in [9] and used simplified calculations with the addition of heuristic rules to forecast the enrollments. Chen [12] presented a forecasting method based on high order fuzzy time series for forecasting the enrollments of the University of Alabama. Chen [13] and Hwang presented a method based on fuzzy time series to forecast the temperature.

Rice has become the main staple food for almost the entire population of Indonesia. However, in Indonesia, the price of food commodities (rice) often fluctuates in price. Due to the rapid fluctuation of rice prices and the uncertainty in the future, it is necessary to forecast rice prices. This study aims to predict the price of rice in the city of Mataram using the Holt double exponential smoothing method and the Cheng fuzzy time series. The model's performance is based on Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) indicators. Forecasting model based on Holt's double exponential smoothing method, the MSE value is 705967.4994 and the MAPE value is 7.91%. On the other hand, based on Cheng's fuzzy time series method, the performance of the forecasting model based on the MSE indicator is 566312.340 and based on the MAPE value of 6.75%. Based on these results, Cheng's fuzzy time series method is more accurate than Holt's double exponential smoothing method. Keywords: Double Exponential Smoothing Holt, Fuzzy Time Series Cheng, Rice Price, MAPE, MSE

Image segmentation plays an important role in machine vision, image recognition, and imaging applications. Based on the fuzzy c-means clustering algorithm, a kernel-distance-based intuitionistic fuzzy c-means clustering (KIFCM) algorithm is proposed. First, a fuzzy complement operator is used to generate the membership degree whereby the hesitation degree of intuitionistic fuzzy set is generated; second, a kernel-induced function is used to calculate the distance from each point to the cluster center instead of the Euclidean distance; third, a new objective function that includes the hesitation degree is established, and the optimization of the objective function results in new iterative expressions for the membership degree and the cluster center. The proposed KIFCM algorithm is compared with the fuzzy c-means clustering (FCM) algorithm, the kernel fuzzy c-means clustering (KFCM) algorithm, and the intuitionistic fuzzy c-means clustering (IFCM) algorithm in segmenting five images. The experimental results verify the effectiveness and superiority of our proposed KIFCM algorithm.

Fuzzy sets theory cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. In this regard, an intuitionistic fuzzy time series forecasting model is built. In the new model, a fuzzy clustering algorithm is used to divide the universe of discourse into unequal intervals, and a more objective technique for ascertaining the membership function and nonmembership function of the intuitionistic fuzzy set is proposed. On these bases, forecast rules based on intuitionistic fuzzy approximate reasoning are established. At last, contrast experiments on the enrollments of the University of Alabama and the Taiwan Stock Exchange Capitalization Weighted Stock Index are carried out. The results show that the new model has a clear advantage of improving the forecast accuracy.

To solve the problem of fuzzy identification of nonlinear systems, a novel fuzzy identification method based on fuzzy c-means clustering (FCM) algorithm and Gaussian function is proposed. Firstly, fuzzy clustering algorithm is used to divide the input space to obtain the clustering center, then the clustering center is used as the gaussian function center to determine the membership function to obtain the premise parameters of the fuzzy model, and the conclusion parameters of the fuzzy model are identified by recursive least squares (RLS). Finally, three simulation examples are given to verify the effectiveness of the proposed method in identifying T-S fuzzy model.

The storage and calculation of the larger amount of data in the cloud data center brings more abnormal data. In order to accurately and efficiently find anomalies in the data in the cloud data center, this paper proposes an anomaly detection parameter optimization algorithm for data center data. Methods Fuzzy C-means clustering (FCM) algorithm is used for anomaly detection. At the same time, in order to overcome the problem that the fuzzy clustering algorithm is sensitive to the initial value and easy to fall into the local optimum, the adaptive bat algorithm is used to optimize the FCM algorithm. And add distributed entropy and average distance to the algorithm, adaptively adjust the optimization ability of traditional bat algorithm. Finally, experiments prove the detection accuracy of the algorithm.

Enrolment forecasting, which provides information for decision making and budget planning, is important in many ways to higher education. Because of its importance, researchers have proposed many forecasting methods to improve accuracy. Different methods such as genetic algorithm, least square that are used to forecast enrolment of student do not give relatively accurate results. However, obtaining accuracy is not an easy task, as many factors have impacts on enrolment numbers. In this work, a fuzzy time series was developed for efficient enrolment forecasting. The model is made up of four steps which are definition of the universe of discourse and intervals, fuzzification of historical data, establishment of fuzzy relationships and enrolment forecast. The max-min operator was used as universe of discourse and we compared our proposed method with the existing linear regression method. The historical enrolment figures of the University of Agriculture, Abeokuta were used as a data set for testing and were implemented using Visual Basic. The forecasting result of the fuzzy time series method is compared with that of the existing least square method, the fuzzy time series method produces the smallest values of the mean square error (MSE) as compared with the least square method. The application was also used to predict students’ enrolment for the next five years. The proposed method was found to obtain more accurate forecasting results than the existing method.

Fu zzy time series forecasting methods have been widely studied in recent years. This is because fuzzy time series forecasting methods are co mpatib le with flexib le calculat ion techniques and they do not require constraints that exist in conventional time series approaches. Most of the real life time series exhibit periodical changes arising fro m seasonality. These variations are called seasonal changes. Although, conventional time series approaches for the analysis of time series which have seasonal effect are abundant in literature, the number of fuzzy t ime series approaches is limited. In almost all of these studies, membership values are ignored in the analysis process. This affects forecasting performance of the approach negatively due to the loss of information as well as posing a situation that is incompatible with the basic features of fuzzy set theory. In this study, for the first time in literature, a new seasonal fuzzy time series approach which considers membership values in both identificat ion of fuzzy relat ions and defuzzification steps was proposed. In the proposed method, we used fuzzy C-means clustering method in fuzzification step and artificial neural networks (ANN) in identification of fu zzy relation and defuzzification steps which consider membership values. The proposed method was applied to various seasonal fuzzy time series and obtained results were compared with some conventional and fuzzy time series approaches. In consequence of this evaluation, it was determined that forecasting performance of the proposed method is satisfactory.

Box–Cox together with our newly proposed transformation were implemented in three different real world empirical problems to alleviate noisy and the volatility effect of them. Consequently, a new domain was constructed. Subsequently, universe of discourse for transformed data was established and an approach for calculating effective length of the intervals was then proposed. Considering the steps above, the initial forecasts were performed using frequently used fuzzy time series (FTS) methods on transformed data. Final forecasts were retrieved from initial forecasted values by proper inverse operation. Comparisons of the results demonstrate that the proposed method produced more accurate forecasts compared with existing FTS on original data.

A new approach based on artificial neural networks for high order multivariate fuzzy time series

In this paper, linking with the basic principle of FCM algorithm, on the basis of theory research, a method of the cluster analysis that FCM and the genetic algorithm are combined together is proposed. Firstly, the approximate optimal solution obtained by the genetic algorithm is taken as the original value of the FCM algorithm, then carrying on the local search to obtain the global optimal solution, the final segmentation result is achieved at last. The experiment results prove that in the view of the flame image segmentation, this method shows the good performance, and has the widespread serviceability, so it is the practical method in image segmentation.