  Many works have been done to handle the uncertainties in the data using type
1 fuzzy regression. Few type 2 fuzzy regression works used interval type 2 for
indeterminate modeling using type 1 fuzzy membership. The current survey
proposes a triangular type-2 fuzzy regression (TT2FR) model to ameliorate the
efficiency of the model by handling the uncertainty in the data. The triangular
secondary membership function is used instead of widely used interval type
models. In the proposed model, vagueness in primary and secondary fuzzy sets is
minimized and also, a specified x-plane of observed value is included in the
same {\alpha}- plane of the predicted value. Complex calculations of the type-2
fuzzy (T2F) model are simplified by reducing three dimensional type-2 fuzzy set
(3DT2FS) into two dimensional interval type-2 fuzzy (2DIT2F) models. The
current survey presents a new regression model of T2F by considering the more
general form of T2F membership functions and thus avoids high complexity. The
performance of the developed model is evaluated using the TAIEX and COVID-19
forecasting datasets. Our developed model reached the highest performance as
compared to the other state-of-art techniques. Our developed method is ready to
be tested with more uncertain data and has the potential to use to predict the
weather and stock prediction.
