Abstract
Fuzzy inference systems have been commonly used for time series forecasting in the literature. Adaptive network fuzzy inference system, fuzzy time series approaches and fuzzy regression functions approaches are popular among fuzzy inference systems. In recent years, intuitionistic fuzzy sets have been preferred in the fuzzy modeling and new fuzzy inference systems have been proposed based on intuitionistic fuzzy sets. In this paper, a new intuitionistic fuzzy regression functions approach is proposed based on intuitionistic fuzzy sets for forecasting purpose. This new inference system is called an intuitionistic fuzzy time series functions approach. The contribution of the paper is proposing a new intuitionistic fuzzy inference system. To evaluate the performance of intuitionistic fuzzy time series functions, twenty-three real-world time series data sets are analyzed. The results obtained from the intuitionistic fuzzy time series functions approach are compared with some other methods according to a root mean square error and mean absolute percentage error criteria. The proposed method has superior forecasting performance among all methods.
Highlights
Forecasting is very important for future planning in many technological areas
Forecasting techniques are attracted by managers and other decision-makers
Fuzzy inference systems and fuzzy time series methods can be used for forecasting
Summary
Forecasting is very important for future planning in many technological areas. Forecasting techniques are attracted by managers and other decision-makers. Aladag et al (2016) proposed a type 1 fuzzy time series function method based on binary particle swarm optimization. There are many fuzzy time series forecasting methods. Bisht and Kumar (2019) used hesitant fuzzy sets based on the computational method for financial time series forecasting. Gupta and Kumar (2019a) proposed a novel highorder fuzzy time series forecasting method based on probabilistic fuzzy sets. Gupta and Kumar (2019b) proposed a hesitant probabilistic fuzzy set-based time series forecasting method. Olej and Hajek (2010a) proposed an intuitionistic fuzzy inference system design for prediction of ozone time series. Bas et al (2019) proposed a type 1 fuzzy function method based on ridge regression for forecasting. The membership and non-membership values are obtained from intuitionistic fuzzy c-means as in Chaira (2011).
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