Fuzzy Logical Relationship Groups Research Articles

Pendekatan Fuzzy Pada Peramalan Jumlah Kunjungan Wisatawan Mancanegara ke Kabupaten Badung

This work is directed to forecast the number of foreign visitors come to tourist’s destinations at Badung regency, Province of Bali. Using visit historical data for period January 2000 to February 2015, Markov Transition Matrix and Fuzzy Triangular Number are applied to represent fuzzy logical relationship group and member function in fuzzy model, respectively. The results showed the in-sample forecasting accuracy for our fuzzy model as much as 2,48 percent. To validate the model, we found the average forecasting error rate for five consecutive months (March – July 2015) as much as 2,70 percent.

Fuzzy time series forecasting based on fuzzy logical relationships and similarity measures

In this paper, we propose a new fuzzy time series forecasting method for forecasting the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy time series, fuzzy logical relationships, particle swarm optimization techniques, the K-means clustering algorithm, and similarity measures between the subscript of the fuzzy set of the fuzzified historical testing datum on the previous trading day and the subscripts of the fuzzy sets appearing in the current states of the fuzzy logical relationships in the chosen fuzzy logical relationship group. The particle swarm optimization techniques are used to get the optimal partition of the intervals in the universe of discourse. The K-means clustering algorithm is used to cluster the subscripts of the fuzzy sets of the current states of the fuzzy logical relationships to get the cluster center of each cluster and to divide the constructed fuzzy logical relationships into fuzzy logical relationship groups. The experimental results show that the proposed fuzzy forecasting method gets higher forecasting accuracy rates than the existing methods. The advantages of the proposed fuzzy forecasting method is that it uses the particle swarm optimization techniques to get the optimal partition of the intervals in the universe of discourse and uses the K-means clustering algorithm to cluster the subscripts of the fuzzy sets of the current states of the fuzzy logical relationships to get the cluster center of each cluster and to divide the constructed fuzzy logical relationships into fuzzy logical relationship groups for increasing the forecasting accuracy rates.

Inventory Management and Pricing Decisions for a Supply Chain with Demand Leakage and a Return-Policy Contract

Fuzzy time series relies heavily on the length of intervals and the formulation of fuzzy relationships. This study proposes a combination of fuzzy c-means (FCM) and frequency-weighted fuzzy logical relationship groups (FLRG) to overcome these problems. It is noted that FCM simplifies the process of fuzzification as it avoids the subjective measures to determine the interval length. However, the results of FCM is sensitive to initial values and it is easily trapped in the local minima. To overcome this, we propose a method to identify the initial centers. The frequency-weighted FLRG is deemed reasonable as the frequency indicates the likelihood of the occurrence of the fuzzy logical relationships (FLR) in the future. To examine the effect of these two factors on the modeling accuracy, the performance of the proposed model is compared to the existing models of equal interval length with no weights, frequency-and-recentness-weights, and a modified model that takes equal interval length with frequency-weights. The results are verified by performing the procedures with various number of groups on the enrollment data as well as some stock indexes. The results show that the proposed method outperforms the other models on these data.

The Hybrids Algorithm Based on Fuzzy Cognitive Map for Fuzzy Time Series Prediction

Fuzzy logic relationship groups are crucial to fuzzy time series prediction. In generally, fuzzy logic relationship groups can be constructed by manually mining fuzzy logic relationship between adjacent data in time series. However, for the large-scale or long-term time series, the way of manually constructing fuzzy logical relationship groups is difficult and infeasible. In this paper, a hybrids prediction algorithm based on Fuzzy Cognitive Map (FCM) is proposed, in which fuzzy c-means clustering algorithm is used to construct the framework of FCM and genetic algorithm (GA) is applied to learn weights of FCM. Finally, a fully learned fuzzy cognitive map is used to represent, store fuzzy logic relationships of fuzzy time series and realize prediction. A benchmark time series — the enrollments of University of Alberta time series is applied to validate the feasibility and effectiveness of the proposed algorithm, whose results show that the proposed prediction algorithm based on FCM is effective and can obtain the satisfactory prediction precision. It is a potential virtue that the proposed algorithm can automatically process the prediction problem of the large-scale or long-term time series.

Introducing polynomial fuzzy time series

Using polynomial concept and non-liner optimization enhanced the performance of Chen's 1996 and Yu's 2005b methods as the two frequently used methods in fuzzy time series model. To this end, polynomial schemes were given to each fuzzy logical relationship groups that had been established through forecast process to establish non-linear optimization systems. The optimal solutions of this system were applied in corresponding steps of algorithms to obtain new weights. To validate model reliability and its effectiveness, the forecasts of two huge databases namely 5 years Taiwan's stock index and 2010 load data of Power Supply Company in Johor Bahru in Malaysia were then exposed to the proposed model. Next, the forecasts were compared with real values in testing datasets. The evaluation of measuring criteria namely RMSEs and MAPEs showed that the proposed model could produce accurate forecast compared with the Chen's and Yu's method in fuzzy time series. The implication of this study is to generalize the results to other fuzzy time series models.

TAIEX Forecasting Based on Fuzzy Time Series and Fuzzy Variation Groups

In this paper, we present a new method to forecast the daily Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy time series and fuzzy variation groups, where the main input factor is the previous day's TAIEX, and the secondary factor is either the Dow Jones, the NASDAQ, the M 1b, or their combination. First, the proposed method fuzzifies the historical training data of the TAIEX into fuzzy sets to form fuzzy logical relationships. Second, it groups the fuzzy logical relationships into fuzzy logical relationship groups (FLRGs) based on the fuzzy variations of the secondary factor. Third, it evaluates the leverage of the fuzzy variations between the main factor and the secondary factor to construct fuzzy variation groups. Fourth, it gets the statistics of the fuzzy variations appearing in each fuzzy variation group. Fifth, it calculates the weights of the statistics of the fuzzy variations appearing in each fuzzy variation group, respectively. Finally, based on the weights of the statistics of the fuzzy variations appearing in the fuzzy variation groups and the FLRGs, it performs the forecasting of the daily TAIEX. Because the proposed method uses both fuzzy variation groups and FLRGs to analyze in detail the historical training data, it gets higher forecasting accuracy rates to forecast the TAIEX than the existing methods.

Handling forecasting problems based on high-order fuzzy logical relationships

People usually use many methods to predict the weather, the temperature, the stock index, the enrollments, the earthquake, the economy, etc. Based on these forecasting results, people can prevent damages to occur or get benefits from the forecasting activities. In this paper, we present a new method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), the enrollments of the University of Alabama and the inventory demand based on high-order fuzzy logical relationships. First, the proposed method fuzzifies the historical data into fuzzy sets to form high-order fuzzy logical relationships. Then, it calculates the value of the variable between the subscripts of adjacent fuzzy sets appearing in the antecedents of high-order fuzzy logical relationships. Then, it lets the high-order fuzzy logical relationships with the same variable value form a high-order fuzzy logical relationship group. Finally, it chooses a high-order fuzzy logical relationship group to forecast the TAIEX. The proposed method gets a higher average forecasting accuracy rate to forecast the TAIEX, the enrollments of the University of Alabama and the inventory demand than the existing methods.