Forecast combination approach with meta-fuzzy functions for forecasting the number of immigrants within the maritime line security project in Turkey

Cevik, Fatma Carman; Gever, Basak; Tak, Nihat; Khaniyev, Tahir

doi:10.1007/s00500-022-07800-7

Cited by 3 publications

(1 citation statement)

References 42 publications

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“…Their objective was to improve the forecasting performance by integrating multiple forecasting techniques within the framework. Cevik et al 28 used the MFF approach to forecast the number of immigrants within the maritime line. Tak 29 used the MFF approach to forecast combination.…”

Section: Introductionmentioning

confidence: 99%

An adaptive categorical effect size method based on intuitionistic meta fuzzy functions

Yabacı Tak

2023

Sci Rep

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There are several categorical effect size methods in the literature. It is not clear which method performs better for a given dataset and it is a challenging task to select the correct method for a given dataset. In this sense, to overcome the questions like “Which method should we choose?” and “Which categorical effect size method is more reliable for a given dataset?”, an adaptive categorical effect size method based on intuitionistic meta fuzzy functions is introduced in the paper. Thus, the main motivation of the proposed method is to obtain more accurate outcomes by combining the results of better performing methods instead of relying on only one method. In the study, the intuitionistic fuzzy c-means clustering algorithm is adapted to meta fuzzy functions by incorporating not only membership degrees but also non-membership degrees to improve the clustering accuracy of meta fuzzy functions. Meta fuzzy functions are the linear combination of seven categorical effect size methods and the weights, which are calculated from membership grades from intuitionistic fuzzy c-means algorithm. Among the functions, the one with the lowest mean absolute percentage error is selected as the best. To evaluate the performance of the proposed method, 2 × 3, 2 × 4, and 3 × 4 contingency tables were simulated. Additionally, the performance of the proposed method is also assessed by applying it to a real-time dataset. Experimental results show that the proposed method outperforms compared to the evaluated seven categorical effect size methods in terms of mean absolute percentage error. Also, the calculated effect sizes are within the range of ±10% in terms of bias. Thus, the results verified that proposed method achieves greater reliability.

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Section: Introductionmentioning

confidence: 99%

An adaptive categorical effect size method based on intuitionistic meta fuzzy functions

Yabacı Tak

2023

Sci Rep

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A fuzzy Gaussian process regression function approach for forecasting problem

Egrioglu,

Bas,

Chen

2024

Granul. Comput.

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A fuzzy regression function approach is a fuzzy inference system method whose rules cannot be determined based on expert opinion, unlike a classical fuzzy inference system. In a fuzzy regression function approach, an input matrix consists of memberships obtained by the fuzzy clustering method and lagged variables of the time series. In the fuzzy regression function approach, the output vector corresponding to this input matrix is also created and the parameter estimation for the method is carried out with the ordinary least square method. As it is known, the ordinary least square method assumes that the data are linear. In addition, although it is very useful to include a priori information describing the formation of the data in the model, in most cases this information is not available. It is also inappropriate to use a model that does not accurately characterize the data. However, it is not appropriate to estimate parameters for nonlinear data using the ordinary least square method. One of the methods to be used in such a situation is the Gaussian process regression method. While the parameters of a selected basis function are fitted in the ordinary least squares regression method, how all measured data are related is determined in the Gaussian process regression. Besides, Gaussian process regression is a Bayesian approach, it can provide uncertainty measurements on forecasts. In this study, a fuzzy Gaussian process regression function is proposed. The contribution of this paper is to propose a new fuzzy inference system that can be used to solve nonlinear data by proposing a fuzzy Gaussian process regression function. The performance of the newly proposed method is evaluated based on the closing values of the Bitcoin and Crude oil time series. The performance comparison of the proposed method is evaluated with many different forecasting methods and it is concluded that the proposed method has superior forecasting performance.

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Comparative Study on Performance of Various Neural Network Algorithms in Construction Project Cost Prediction

Li,

Zhao,

Zhong

et al. 2024

Int. j. math. eng. manag. sci.

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Making accurate predictions of the construction cost is essential for ensuring the smooth implementation of projects and guaranteeing economic benefits. The problem to be studied in this article is how to predict construction project costs accurately. The related factors affecting construction project costs are briefly introduced in this paper. A back-propagation neural network (BPNN) was proposed to predict construction engineering costs, and the AdaBoost algorithm was used to improve it. Then, simulation experiments were carried out. It was found that the Adaboost-BPNN algorithm converged to stability faster, and the mean square error was smaller (10-5) when it was stable. Compared with the support vector machine and traditional BPNN algorithms, the AdaBoost-BPNN algorithm had better goodness of fit (0.787) and provided more accurate prediction results for construction engineering costs (mean average error: 0.467, root-mean-square error: 1.118). The novelty of this article lies in utilizing AdaBoost to combine multiple weak predictors into a strong predictor, thereby enhancing the performance of the BPNN algorithm. The contribution lies in improving the predictive performance of the BPNN through the combination principle of AdaBoost, providing an effective reference for accurate cost prediction in construction engineering.

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Forecast combination approach with meta-fuzzy functions for forecasting the number of immigrants within the maritime line security project in Turkey

Cited by 3 publications

References 42 publications

An adaptive categorical effect size method based on intuitionistic meta fuzzy functions

An adaptive categorical effect size method based on intuitionistic meta fuzzy functions

A fuzzy Gaussian process regression function approach for forecasting problem

Comparative Study on Performance of Various Neural Network Algorithms in Construction Project Cost Prediction

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