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

Pattanayak, Radha Mohan; Behera, Himansu Sekhar; Panigrahi, Sibarama

doi:10.1007/s13369-021-05718-0

Cited by 10 publications

(1 citation statement)

References 41 publications

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“…After that, some types of NSs have been proposed (Lupiáñez, 2009;Wang et al, 2010;Ye, 2014;Ulucay et al, 2018;Liu and Cheng, 2020;Edalatpanah, 2020;Luo al., 2022). Furthermore, in neutrosophical literature, several applications such as facility location and routing (Deveci et al, 2021), SSP (Pamucar et al, 2020), social failure detection (Torkayesh et al, 2022), linear programming (Edalatpanah, 2019;Das et al, 2021) and , time series models (Pattanayak et al, 2022), DEA ( Mao et al, 2020), SPP (Kumar et al, 2019), MCDM (Deli et al, 2021), etc. have been addressed.…”

Section: Neutrosophic Logicmentioning

confidence: 99%

A neutrosophical model for optimal sustainable closed-loop supply chain network with considering inflation and carbon emission policies

Kalantari

Kazemipoor

Sobhani

et al. 2022

Decis. Mak. Appl. Manag. Eng.

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In this paper, a stable CLSC problem is modeled in conditions of uncertainty and indeterminacy. The SCN is designed to maximize NPV and minimize carbon releases by maintaining environment friendly policies and accounting for increase. To achieve a suitable model for designing a stable CLSCN and making important decisions such as selecting the right suppliers, selecting the type of transport, initial the facility, the optimal flow between facilities and accomplishing an efficient solution to the problem decision making, the neutrosophic optimization method is used. The results of experiments that discuss and evaluate different scenarios confirm the efficiency and validity of the proposed model. The findings also show that the effective improvement of the obtained solutions by reducing the solution time up to twenty percent can be responsible for large-scale problems in different scenarios. This paper uses a neutrosophic optimization method to solve the problem of designing a stable CLSCN under uncertainty and indeterminacy.

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Section: Neutrosophic Logicmentioning

confidence: 99%

A neutrosophical model for optimal sustainable closed-loop supply chain network with considering inflation and carbon emission policies

Kalantari

Kazemipoor

Sobhani

et al. 2022

Decis. Mak. Appl. Manag. Eng.

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A study and development of high‐order fuzzy time series forecasting methods for air quality index forecasting

Pradhan,

Panigrahi

2024

Journal of Forecasting

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The endless adverse effects of air pollution incidents have raised significant public concerns in the past few decades. The measure of air pollution, that is, the air quality index (AQI), is highly volatile and associated with different kinds of uncertainties. Following this, the study and development of accurate fuzzy time series forecasting (TSF) methods for predicting the AQI have a significant role in air pollution control and management. Motivated by this, in this paper, a systematic study is made to evaluate the true potential of fuzzy TSF methods employing traditional fuzzy set (TFS), intuitionistic fuzzy set (IFS), hesitant fuzzy set (HFS), and neutrosophic fuzzy set (NFS) in forecasting the AQI. Two novel high‐order fuzzy TSF methods, TFS‐multilayer perceptron (MLP) and HFS‐MLP, are proposed employing TFS and HFS in which ratio trend variation of AQI data is used instead of original AQI, MLP is used to model the fuzzy logical relationships (FLRs), and none/mean of aggregated membership values are used while modeling the FLRs using MLP. The results from the proposed fuzzy TSF methods are compared with recently proposed fuzzy TSF methods employing TFS, IFS, and NFS and six popular machine learning models, including MLP, support vector regression (SVR), Bagging Regressors, XGBoost, long‐short term memory (LSTM), and convolutional neural network (CNN). The “Wilcoxon Signed‐Rank test” and “Friedman and Nemenyi hypothesis test” are applied to the results obtained by employing different ratios in the train‐validation‐test to draw decisive conclusions reliably. The simulation results show the statistical dominance of the proposed TFS‐MLP method over all other crisp and fuzzy TSF methods employed in this paper.

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Forecasting Crude Oil Prices: A Machine Learning Perspective

Purohit,

Panigrahi

2024

Communications in Computer and Information Science

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

Cited by 10 publications

References 41 publications

A neutrosophical model for optimal sustainable closed-loop supply chain network with considering inflation and carbon emission policies

A neutrosophical model for optimal sustainable closed-loop supply chain network with considering inflation and carbon emission policies

A study and development of high‐order fuzzy time series forecasting methods for air quality index forecasting

Forecasting Crude Oil Prices: A Machine Learning Perspective

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