Forecasting Monthly Housing Sales to Foreigners with Type 1 Fuzzy Regression Functions Approach Based on Ridge Regression
Abstract
Artificial neural networks, fuzzy inference systems, and hybrid methods where these methods are used together have been frequently used in forecasting problems. Although fuzzy inference systems produce very effective results in forecasting problems, the fact that many classical fuzzy inference systems depend on the rule base makes it difficult to implement these methods. The type 1 fuzzy regression functions approach, which is not dependent on the rule base and has a simpler structure than many fuzzy inference systems, is frequently used in forecasting problems. Although the Type 1 fuzzy regression functions approach has superior forecasting performance, it is known that the method has a multicollinearity problem in the application process of this method. The type 1 fuzzy regression functions approach based on ridge regression both eliminates the multicollinearity problem of the Type 1 fuzzy regression functions approach and produce better forecasting results than the Type 1 fuzzy regression functions approach. In this study, the forecasting of monthly house sales to foreigners is carried out for the first time with the Type 1 fuzzy regression functions approach based on ridge regression, and the results of the analysis are compared with many methods suggested in the literature. As a result of the analysis, it is concluded that the forecasting results obtained with the Type 1 fuzzy regression functions approach based on ridge regression produce better results than some other methods in the literature.
Keywords
Fuzzy inference systems, Type 1 fuzzy regression functions approach, Ridge regression, Forecasting, Housing sales
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