Abstract
Intuitionistic fuzzy time series (IFTS) forecasting models have been proved more ideal than other forecasting models because of their efficient handling of non-stochastic uncertainties with non-determinism. It has also been shown by many researchers that interval length in both fuzzy time series (FTS) and IFTS plays a pivot role and affects accuracy in forecasting. In this paper, we propose a novel method to optimize interval length for enhancing accuracy in IFTS forecasting models. Proposed method optimizes interval length by minimizing mean square errors (MSEs). Objective function considered in this study is an interpolating polynomial which is calculated by Newton divided difference method. Performance and accuracy of proposed method is validated by comparing it with model of Kumar and Gangwar (IEEE Trans Fuzzy Syst 2015:2507582, [1]) and other previous existing FTS and IFTS forecasting models using time series data of University of Alabama enrollments.
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Pant, M., Shukla, A.K., Kumar, S. (2021). A Novel Method to Optimize Interval Length for Intuitionistic Fuzzy Time Series. In: Tiwari, A., Ahuja, K., Yadav, A., Bansal, J.C., Deep, K., Nagar, A.K. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1393. Springer, Singapore. https://doi.org/10.1007/978-981-16-2712-5_5
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DOI: https://doi.org/10.1007/978-981-16-2712-5_5
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