A multivariate model of fuzzy integrated logical forecasting method (M-FILF) and multiplicative time series clustering: A model of time-varying volatility for dry cargo freight market

The aim of this paper is to improve the fuzzy logical forecasting model (FILF) by utilizing multivariate inference and the partitioning problem for an exponentially distributed time series by using a multiplicative clustering approach. Fuzzy time series (FTS) is a growing study field in computer science and its superiority is indicated frequently. Since the conventional time series analysis requires various pre-conditions, the FTS framework is very useful and convenient for many problems in business practice. This paper particularly investigates pricing problems in the shipping business and price–volatility relationship is the theoretical point of the proposed approach. Both FTS and conventional time series results are comparatively presented in the final section and superiority of the proposed method is explicitly noted.

► The fuzzy integrated logical forecasting (FILF) model is improved for multivariate case.
► M-FILF is used for investigation of time-varying volatility in dry cargo shipping.
► A multiplicative time series clustering method is presented for exponentially increasing volatility series.
► The conventional GARCH model is limited to response instant fluctuations.
► M-FILF indicated superior forecasting results in both cumulative and instant accuracy control.