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A Non-Probabilistic Neutrosophic Entropy-Based Method For High-Order Fuzzy Time-Series Forecasting

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Abstract

Over the years, numerous fuzzy time-series forecasting (FTSF) models have been developed to handle the uncertainty and non-determinism in the time-series (TS) data. To handle the non-determinism and indeterminacy, researchers have considered either intuitionistic fuzzy set or hesitant fuzzy set theory. However, in both the fuzzy set theories (FST), the degree of indeterminacy is a dependent value and always lies in the range [0, 1]. Hence, these two fuzzy set theories fail to model the indeterminacy value when the degree of non-membership fluctuates due to hesitancy. Motivated from this, we have considered neutrosophic entropy-based fuzzy time-series forecasting (NEBFTSF) model where the neutrosophic entropy of each observation in the TS is used to capture the indeterminacy. Apart from this, the triangular membership value for each observation is used to illustrate the non-probabilistic uncertainty in the TS. The present research mainly focuses on three concepts such as 1) an adaptive method is used to partition the universe of discourse (UOD) into unequal length of intervals (LOIs), 2) for the first time the fuzzy logical relationships (FLRs) are established by considering the ratio trend variation (RTV) data with mean of aggregated entropy value of each crisp observation, and 3) to obtain the forecasted values both de-trending and de-normalization are employed. To assess the forecasting performance of the proposed model, 11 TS datasets with ten distinct profound forecasting models are considered. The Friedman and Nemenyi hypothesis test and Wilcoxon signed rank test conform the forecasting efficiency and reliability of the NEBFTSF model.

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Authors and Affiliations

  1. Department of Information Technology, Veer Surendra Sai University of Technology, Burla, Odisha, 768018, India

    Radha Mohan Pattanayak & H. S. Behera

  2. Department of Computer Sc. Engineering and Application, Sambalpur University Institute of Information Technology, Burla, Odisha, India

    Sibarama Panigrahi

Authors
  1. Radha Mohan Pattanayak
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  2. H. S. Behera
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  3. Sibarama Panigrahi
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Correspondence to H. S. Behera.

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Pattanayak, R.M., Behera, H.S. & Panigrahi, S. A Non-Probabilistic Neutrosophic Entropy-Based Method For High-Order Fuzzy Time-Series Forecasting. Arab J Sci Eng 47, 1399–1421 (2022). https://doi.org/10.1007/s13369-021-05718-0

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