Accurate Prediction Using Triangular Type-2 Fuzzy Linear Regression

TL;DR

This paper introduces a triangular type-2 fuzzy regression model that effectively handles data uncertainty, simplifies complex calculations, and outperforms existing methods in forecasting datasets like TAIEX and COVID-19.

Contribution

The paper proposes a novel triangular type-2 fuzzy regression model that reduces computational complexity and improves prediction accuracy over existing fuzzy regression techniques.

Findings

Achieved highest performance on TAIEX and COVID-19 datasets.

Simplified type-2 fuzzy set calculations by reducing dimensions.

Demonstrated potential for weather and stock market predictions.

Abstract

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.