The present research proposes a novel probabilistic intuitionistic fuzzy time series forecasting (PIFTSF) model using support vector machine (SVM) to address both uncertainty and non-determinism associated with real world time series data. In this model, the probability of membership values of crisp observation is determined to handle the statistical uncertainty. At the same time, the intuitionistic fuzzy element of crisp observation is determined to handle the non-statistical uncertainty along with non-determinism. Then, both the membership values are aggregated to obtain the probabilistic intuitionistic fuzzy element which handles both statistical and non-statistical uncertainty along with non-determinism due to hesitancy. Additionally, a novel trend-based discretization (TBD) method is proposed to determine the universe of discourse and number of intervals (NOIs) of fuzzy time series (FTS). For the first time, the fuzzy logical relationships (FLRs) are established for the probabilistic intuitionistic fuzzy set by considering the ratio trend variation (RTV) of crisp observation along with the mean of aggregated membership values which is modelled using SVM. The efficiency of the proposed PIFTSF model is demonstrated with sixteen diversified time series datasets and seven existing FTS models. A sensitivity analysis is carried out with respect to different design strategies to ensure the robustness of the proposed model. Extensive statistical analyses on obtained results confirm the superiority of the proposed model over other existing models. Further, Wilcoxon signed rank test, and Friedman and Nemenyi hypothesis test ensures the accuracy, robustness and reliability of the proposed model against its counterparts.

本研究提出了一种使用支持​​向量机（SVM）的新型概率直觉模糊时间序列预测（PIFTSF）模型，以解决与现实世界时间序列数据相关的不确定性和不确定性。在该模型中，确定了清晰观测的隶属度值的概率以处理统计不确定性。同时，确定了清晰观测的直觉模糊元素来处理非统计不确定性和不确定性。然后，将两个隶属度值进行聚合，以获得概率直觉模糊元素，该元素可同时处理统计和非统计不确定性以及由于犹豫而引起的不确定性。另外，提出了一种新颖的基于趋势的离散化（TBD）方法，用于确定话语范围和模糊时间序列（FTS）的区间数（NOI）。第一次，通过考虑清晰观测的比率趋势变化（RTV）以及使用SVM建模的聚合成员值的平均值，为概率直觉模糊集建立了模糊逻辑关系（FLR）。PIFTSF模型的效率通过16个多样化的时间序列数据集和7个现有的FTS模型得到了证明。针对不同的设计策略进行了敏感性分析，以确保所提出模型的鲁棒性。对获得的结果进行广泛的统计分析，证实了该模型优于其他现有模型的优势。此外，Wilcoxon进行了等级测试，