Fuzzy time series models have been applied to handle nonlinear problems. To forecast fuzzy time series, this study applies a backpropagation neural network because of its nonlinear structures. We propose two models: a basic model using a neural network approach to forecast all of the...

The size of underground economy (UE) expansion usually increases the tax gap, impose a burden on the economy, and results in tax distortions. This study uses the MIMIC approach to model the causal variables and indicating variables to estimate the UE in Taiwan. We also focus on testing the data...

The size of the ‘underground economy’ (UE) is valuable information in the formulation of macroeconomic and fiscal policy. This study applies fuzzy set theory and fuzzy logic to model Taiwan's UE over the period from 1960 to 2003. Two major factors affecting the size of the UE, the effective...

Most conventional fuzzy time series models (Type 1 models) utilize only one variable in forecasting. Furthermore, only part of the observations in relation to that variable are used. To utilize more of that variable's observations in forecasting, this study proposes the use of a Type 2 fuzzy...

Fuzzy time-series models have been used to model observations, where each one of them contains multiple values. The formulation of fuzzy relationships and the lengths of intervals are considered to be two of the critical factors that affect forecasting results. Unfortunately, the lengths of the...

This study proposes weighted models to tackle two issues in fuzzy time series forecasting, namely, recurrence and weighting. It is argued that recurrent fuzzy relationships, which were simply ignored in previous studies, should be considered in forecasting. It is also recommended that different...