Plithogenic Soft Set Models Based on Neutrosophic and Intuitionistic Fuzzy Sets for Multi-Attribute Time Series Forecasting which subject

Abstract

Forecasting multi-attribute time series (MATS) data remains a challenging task owing to complex data patterns and various sources of uncertainty. To address this, this study proposes two advanced forecasting models, plithogenic-intuitionistic fuzzy soft set (P-IFSS) and plithogenic-neutrosophic soft set (P-NSS), which extend conventional soft set frameworks by integrating the plithogenic concept. This integration enables a more effective representation of the degree of contradiction among the attributes, thereby improving the accuracy and robustness of the forecast. Using Indonesian bond-yield data as a case study, the performance of the proposed models was evaluated against traditional intuitionistic fuzzy soft set (IFSS) and neutrosophic soft set (NSS) models. The experimental results demonstrated that both P-IFSS and P-NSS consistently outperformed their non-plithogenic counterparts across multiple training periods and parameter settings. The findings highlight the potential of plithogenic soft set extensions as an effective framework for handling complex uncertainties in multi-attribute time-series forecasting, particularly in financial data analysis and decision-making applications.

Keywords Plithogenic soft set, Intuitionistic fuzzy set, Neutrosophic soft set, Time-series forecasting, Bond yield, Uncertainty modeling