Use of Intuitionistic Fuzzy Time Series in Forecasting Enrollments to an Academic Institution

Joshi, Bhagawati Prasad; Pandey, Mukesh; Kumar, Sanjay

doi:10.1007/978-981-10-0448-3_70

Cited by 35 publications

(4 citation statements)

References 15 publications

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“…The intuitionistic fuzzy c-means algorithm was utilized to introduce the modeling and implementation of intuitionistic FTS. Various Intuitionistic FTS approaches were proposed (Joshi et al 2016;Wang et al 2016;Fan et al 2016;Kumar and Gangwar 2016). Using triangle membership functions with equal and unequal intervals, Bisht and Kumar (2016) created hesitant FSs.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid EMPSO-PFTS Method for Fuzzy Time Series Forecasting: Integrating Picture Fuzzy Sets and Exponentially Mutated Particle Swarm Optimization

Dutta,

Rath

2023

Preprint

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Current research has demonstrated that fuzzy sets can be used to address forecasting issues. Researchers have created numerous fuzzy time series (FTS) approaches without taking into account the non-determinacy. For a considerable period, researchers have consistently focused on two significant key issues: determining the optimal interval size and incorporating non-determinacy. The focus of this article is to present a groundbreaking picture fuzzy time series (PFTS) forecasting model that is constructed based on the principles of picture fuzzy sets (PFSs). A PFS represents a generalized form of fuzzy and intuitionistic fuzzy sets. Here picture fuzzy clustering (PFC) technique is utilized for the construction of PFS. In this article, we integrate PFS and exponentially mutated particle swarm optimization (EMPSO) to develop a novel hybrid EMPSO-PFTS forecasting method. Optimal length is determined by EMPSO, and non-determinacy is taken into account by PFS when time series data is fuzzy. The suggested forecasting method is used on data sets from the University of Alabama and the market price of the State Bank of India (SBI-P) at the Bombay Stock Exchange, India, to demonstrate its applicability and usefulness. Mean square error (MSE) and average forecasting error (AFE) are used to gauge the effectiveness of the proposed method. The significant reduction in both MSE and AFE is strong evidence of the superior performance of the proposed EMPSO-PFTS method compared to various existing methods. To ensure the reliability and validity of the proposed method, rigorous statistical validation and performance analysis are conducted.

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Section: Introductionmentioning

confidence: 99%

A Novel Hybrid EMPSO-PFTS Method for Fuzzy Time Series Forecasting: Integrating Picture Fuzzy Sets and Exponentially Mutated Particle Swarm Optimization

Dutta,

Rath

2023

Preprint

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“…The analysis involves the use of models such as autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), vector autoregressive (VAR) and vector autoregressive moving average(VARMA) models. Others which have been used lately include Fuzzy time series (FTS) models as seen in [3], [4]. In this work, seasonal autoregressive integrated moving average(SARIMA) modeling approach which caters for the seasonality component of a time series when forecasting is adopted.…”

Section: Introductionmentioning

confidence: 99%

Time Series Modeling of Monetary Value from Kenya’s Horticultural Export Produce

Michael,

David,

David

2023

AJPAS

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Kenya’s horticulture sector is one of the key contributors to the country’s national income. In this paper, we apply Box-Jenkins SARIMA time series modeling approach to develop a time series model that best describes the income to Kenya’s economy from the export of horticulture produce. In the process of analysis, we considered monthly data from August 1998 to March 2023. It was found that, SARIMA(3; 1; 4)(0; 1; 0)12 is the suitable model that describes the income from the export of Kenya’s horticulture produce.

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“…Egrioglu et al [52] introduced a new intuitionistic fuzzy time series forecasting method based on pi-sigma artificial neural networks and artificial bee colony. Also, some other studies interested in intuitionistic fuzzy time series can be given as Zheng et al [53,54], Wang et al [55], Joshi et al [56], Hu et al [57], Fan et al [58] and Abhishekh et al [59].…”

Section: Introductionmentioning

confidence: 99%

A new explainable robust high-order intuitionistic fuzzy time-series method

2021

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Fuzzy time series forecasting methods based on type-1 fuzzy sets continue to have largely proposed in the literature. These methods use only membership values in determining fuzzy relations. However, Intuitionistic fuzzy time series models basically use both membership values and nonmembership values. So, it can be considered that the using of intuitionistic fuzzy time forecasting models will be able to increase the forecasting performance in the fuzzy time series analyses because of the fact that more information is used. Therefore, Intuitionistic fuzzy time series models have started to use for solving the real-life series in the fuzzy time series literature since 2013. In this study, a new explainable robust high order intuitionistic fuzzy time series forecasting method are proposed based on new defined model. In the proposed method, the algorithm of intuitionistic fuzzy c-means is used for fuzzification of observations and a robust regression method is used to determine fuzzy relations. Because the robust regression is employed to define fuzzy relation, all inputs of the method can be explainable and they can be statistically tested and commented. Applications of this study have been made by using energy data of Primary Energy Consumption (PEC) between 1965 and 2016 for 23 countries in the region of Europe&Eurasia. Forecasting performances obtained from these applications by using the proposed method have been compared with performances of some other time series method in the literature and the results have been discussed.

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Use of Intuitionistic Fuzzy Time Series in Forecasting Enrollments to an Academic Institution

Cited by 35 publications

References 15 publications

A Novel Hybrid EMPSO-PFTS Method for Fuzzy Time Series Forecasting: Integrating Picture Fuzzy Sets and Exponentially Mutated Particle Swarm Optimization

A Novel Hybrid EMPSO-PFTS Method for Fuzzy Time Series Forecasting: Integrating Picture Fuzzy Sets and Exponentially Mutated Particle Swarm Optimization

Time Series Modeling of Monetary Value from Kenya’s Horticultural Export Produce

A new explainable robust high-order intuitionistic fuzzy time-series method

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