Forecasting of the Fuzzy Univariate Time Series by the Optimal Lagged Regression Structure Determined Based on the Genetic Algorithm

Eren, Mirac/0000-0002-5150-9144 WOS: 000438007500012 Estimation obtained through classical regression model reveals the fitting (or prediction) and projection (or forecast) values with a certain error. This situation leads to loss of information and imprecision of data. However, if the imprecise information is converted to fuzzy data rather than single value, an estimation procedure can be obtained in which observation errors are hidden in fuzzy coefficients. Thus, it would be more realistic to make an interval estimate instead of a single value estimate with a certain margin of error. Therefore, in this study, a novel fuzzy least squares method developed for the variables expressed by LR-type fuzzy numbers, based on the optimal classical lagged regression model structure determined by the genetic algorithm, was addressed. a numerical example to explain how the proposed method is applicable was considered.