Pal, S.S. and Kar, S. (2018) Fuzzy Time Series Model for Unequal Interval Length Using Genetic Algorithm. In: Chandra, P., et al., Eds., Information Technology and Applied Mathematics, Springer, 205-216.
https://doi.org/10.1007/978-981-10-7590-2_15

has been cited by the following article:

• TITLE: Software for a Novel Fuzzy Time Series Forecasting Model Based on Balanced Tree Data Structures

  AUTHORS: Thiago Henrique Barbosa de Carvalho Tavares, Bruno Pérez Ferreira, Eduardo Mazoni Andrade Marçal Mendes

  KEYWORDS: Finance, Fuzzy, Statistics, Red-Black Tree, Machine Learning, Deep Learning

  JOURNAL NAME: Journal of Computer and Communications, Vol.13 No.5, May 16, 2025

  ABSTRACT: Predicting stock market trends remains a heavily researched topic due to the inherent complexity of market fluctuations, driven by subjective and often unpredictable factors. These challenges make accurate predictions difficult for traditional models. Fuzzy models address this issue by accounting for the uncertainty of market returns, thereby mitigating the influence of human emotions on predictions. A key component of these models is the use of data structures to define clusters effectively. In this context, this paper introduces a novel fuzzy model that leverages the red-black tree (RBT) data structure to enhance predictive capability. As a balanced binary search tree, the RBT plays a critical role in improving forecasting precision. A comparative analysis with existing fuzzy models from the literature demonstrates the superior performance of the proposed approach.