Outliers detection in selected fuzzy regression models

The paper proposes three fuzzy regression models - concerning temperature and electricity load - based on real data. In the first two models the monthly temperature in a period of four years in a Polish city is analyzed. We assume the temperature to be fuzzy and its dependence on time and on the temperature in the previous month is determined. In the construction of the fuzzy regression models the least square methods was used. In the third model we analyze the dependence of the daily electricity load (assumed to be a fuzzy number) on the (crisp) temperature. Outliers, i.e. non-typical instances in the observations are identified, using a modification of an identification method known from the literature. The proposed method turns out to identify the outliers consistently with the real meaning of the experimental data.