A New Interval Type-2 Fuzzy Logic System Under Dynamic Environment: Application to Financial Investment

Abstract This paper proposes a new interval type-2 fuzzy logic system (IT2 FLS) for financial investment with time-varying parameters adaptive to real-time data streams by using an on-line learning method based on a state-space framework. Particularly, our state-space approach regards the parameters of IT2 FLSs as state variables to sequentially learn by Bayesian filtering algorithms under dynamic environments, where time-series data are continuously observed with occasional structural changes. Moreover, our proposal is effective for financial investment, which often involves various practical complex constraints, because general state-space model makes it possible to flexibly deal with non-linearities. In our empirical experiment with time-series data of global financial assets, our approach is applied to on-line parameter learning of type-1 and type-2 FLSs for portfolio decision making. As a result, it is shown that the IT2 FLS holds its advantage against the type-1 FLS, even though both of the type-1 and type-2 models have the adaptive time-varying parameters, which is an unexplored topic for empirical studies of this area.