A Multivariate Heuristic Model for Fuzzy Time-Series Forecasting

Fuzzy time-series models have been widely applied due to their ability to handle nonlinear data directly and because no rigid assumptions for the data are needed. In addition, many such models have been shown to provide better forecasting results than their conventional counterparts. However, since most of these models require complicated matrix computations, this paper proposes the adoption of a multivariate heuristic function that can be integrated with univariate fuzzy time-series models into multivariate models. Such a multivariate heuristic function can easily be extended and integrated with various univariate models. Furthermore, the integrated model can handle multiple variables to improve forecasting results and, at the same time, avoid complicated computations due to the inclusion of multiple variables.

I. Introduction

Time series has long been an interesting research topic, and various models have been proposed. More recently, fuzzy time-series models, which are based on fuzzy sets, have been advanced to enhance the models' forecasting capabilities and applicability. They have been favored over the conventional models because they can handle nonlinear data directly [12] and because rigid assumptions regarding the data are not needed [19]. These models have been applied to various problem domains, such as enrollment [1], [5], [6], [10], [11], [15], [17], [18], inventory [10], temperature [3], the stock index [5], [6], [8], [9], [10], [21], [22], etc., and many of them have been shown to provide better forecasting results than their conventional counterparts [1], [3], [4], [15], [17]–[19].