A new explainable robust high-order intuitionistic fuzzy time-series method

Abstract

Fuzzy time series, based on type-1 fuzzy sets, continue to have a wide range of use in the literature. These methods use only membership values to determine the fuzzy relations. However, intuitionistic fuzzy time series models use both membership values and non-membership values. So it can be considered that the use of intuitionistic fuzzy time forecasting models will be able to increase the forecasting performance because the intuitionistic fuzzy sets have more information than fuzzy sets. Therefore, intuitionistic fuzzy time series models have started to employ for forecasting the real-life series in the fuzzy time series literature. A novel, explainable, robust high-order intuitionistic fuzzy time series forecasting method is proposed based on a newly defined model. In the proposed method, the intuitionistic fuzzy c-means algorithm is used for the fuzzification of observations, and a robust regression method employed for determining fuzzy relations. With the use of robust regression in determining the fuzzy relationships, all inputs of the proposed method can be explainable and they can be tested and commented on statistically. Applications of this study are made by using energy data of Primary Energy Consumption between the years 1965 and 2016 for 23 countries in the region of Europe-Eurasia. The forecasting performance of the proposed method is compared with the performance of some selected benchmarks, and the obtained results are discussed.