A New Method for Short Multivariate Fuzzy Time Series Based on Genetic Algorithm and Fuzzy Clustering

Selim, Kamal Samy; Elanany, Gihan A.

doi:10.1155/2013/494239

Cited by 6 publications

(5 citation statements)

References 20 publications

(28 reference statements)

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“…In addition, too many intervals causes complexity and reduces the essence of fuzzy time series [26,27,28,29].…”

Section: B Data Processingmentioning

confidence: 99%

Automatic Clustering and Fuzzy Logical Relationship to Predict the Volume of Indonesia Natural Rubber Export

Aprilini,

Rini,

Satria

2023

SJIA

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Natural rubber is one of the pillars of Indonesia's export commodities. However, over the last few years, the export value of natural rubber has decreased due to an oversupply of this commodity in the global market. To overcome this problem, it is possible to predict the volume of Indonesia natural rubber exports. Predicted values can also help the government to compile market intelligence for natural rubber commodities periodically. In this study, the prediction of the export volume of natural rubber was carried out using the Automatic Clustering as an interval maker in the Fuzzy Time Series or usually called Automatic Clustering and Fuzzy Logical Relationship (ACFLR). The data used is 51 data per year from 1970 to 2020. The purpose of this study is to predict the volume of Indonesia natural rubber exports and compare the prediction results between the Automatic Clustering and Fuzzy Logical Relationship (ACFLR) and Chen's Fuzzy Time Series. The results showed that there was a significant difference between the two methods, ACFLR got 0.5316% MAPE with and Chen's Fuzzy Time Series model got 8.009%. Show that the ACFLR method performs better than the pure Fuzzy Time Series in predicting volume of Indonesia natural rubber exports.

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“…In addition, too many intervals causes complexity and reduces the essence of fuzzy time series [26,27,28,29].…”

Section: B Data Processingmentioning

confidence: 99%

Automatic Clustering and Fuzzy Logical Relationship to Predict the Volume of Indonesia Natural Rubber Export

Aprilini,

Rini,

Satria

2023

SJIA

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“…Dalam penelitian ini, prediksi curah hujan DKI Jakarta menggunakan metode FTS dikarenakan pada kemampuan model dalam menangani data deret waktu tanpa perlu memvalidasi teori apapun (Selim & Elanany, 2013). Metode FTS pertama kali digunakan oleh Song & Chissom (1993)…”

Section: Pendahuluanunclassified

Rainfall Prediction Using Fuzzy Time Series

Susano

Anggraeni

2021

ncl

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Flood is one of the problems faced by DKI Jakarta Province. To be able to reduce losses caused by flooding is to predict rainfall. The type of research is quantitative research using rainfall prediction method using Fuzzy Time Series (FTS) which was developed by Stevenson & Porter (2009). The data used is rainfall data at Kemayoran Station in the period January 2018 to December 2020. The prediction accuracy results state that the FTS method has poor accuracy so that improvements are needed in the formation of interval intervals.

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“…In addition, the traffic accident data have been employed in the analysis of several set partitioning models (Lee et al 2007;Jilani and Burney 2008;Egrioglu 2012;Kamal and Gihan 2013). Table 9 shows actual in-sample (trained model) predictions for one of the accident occurrence data that were analysed, while Tables 10, 11 and 12 show the NS-GFMAPR and S-GFMAPR prediction fitness to data used in building the model in comparison with results obtained by the established models previously mentioned.…”

Section: Gfmapr Data Set Partitioning Accuracy In Model Trainingmentioning

confidence: 99%

A novel grey–fuzzy–Markov and pattern recognition model for industrial accident forecasting

2017

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Industrial forecasting is a top-echelon research domain, which has over the past several years experienced highly provocative research discussions. The scope of this research domain continues to expand due to the continuous knowledge ignition motivated by scholars in the area. So, more intelligent and intellectual contributions on current research issues in the accident domain will potentially spark more lively academic, value-added discussions that will be of practical significance to members of the safety community. In this communication, a new grey-fuzzy-Markov time series model, developed from nondifferential grey interval analytical framework has been presented for the first time. This instrument forecasts future accident occurrences under timeinvariance assumption. The actual contribution made in the article is to recognise accident occurrence patterns and decompose them into grey state principal pattern components. The architectural framework of the developed grey-fuzzyMarkov pattern recognition (GFMAPR) model has four stages: fuzzification, smoothening, defuzzification and whitenisation. The results of application of the developed novel model signify that forecasting could be effectively carried out under uncertain conditions and hence, positions the model as a distinctly superior tool for accident forecasting investigations. The novelty of the work lies in the capability of the model in making highly accurate predictions and forecasts based on the availability of small or incomplete accident data.

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A New Method for Short Multivariate Fuzzy Time Series Based on Genetic Algorithm and Fuzzy Clustering

Cited by 6 publications

References 20 publications

Automatic Clustering and Fuzzy Logical Relationship to Predict the Volume of Indonesia Natural Rubber Export

Automatic Clustering and Fuzzy Logical Relationship to Predict the Volume of Indonesia Natural Rubber Export

Rainfall Prediction Using Fuzzy Time Series

A novel grey–fuzzy–Markov and pattern recognition model for industrial accident forecasting

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