Enhanced fuzzy time series forecasting model based on hesitant differential fuzzy sets and error learning

Most of existing fuzzy time series forecasting models lead to unsatisfactory forecasting performance due to deficiencies in constructing fuzzy intervals. This paper adopts the Fuzzy Silhouette criterion to determine the optimal number and length of fuzzy intervals effectively and objectively. The universe of discourse with equal and unequal intervals are merged by aggregating hesitant information. Second, multiple relevant factors are considered to reveal the complex attributes of the real-world data, and differential fuzzy sets are included to reconnoiter deep trends. Finally, an optimal error learning mechanism is applied to enhance the forecasting performance. Key strengths of the developed model are verifying the possibility of increasing the performance of conventional fuzzy time series models by integrating them with new components and processes. Three typical stock index datasets are selected to evaluate the performance of the proposed model and experimental results prove that it outperforms compared models in forecasting accuracy and profitable ability.

Automated summary

The proposed fuzzy time series forecasting model improves performance by optimizing fuzzy interval construction, deep trend analysis, and error learning mechanisms, with experimental results demonstrating superior accuracy and profitability in stock index prediction.