A clustering based forecasting algorithm for multivariable fuzzy time series using linear combinations of independent variables

Askari, S.; Montazerin, Nader; Zarandi, M. H. Fazel

doi:10.1016/j.asoc.2015.06.028

Cited by 58 publications

(34 citation statements)

References 31 publications

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“…In some studies fuzzy C-means (FCM) technique was used by Cheng et al [38], Li et al [74], Aladag et al [6], Alpaslan et al [8], Egrioglu [45], Egrioglu et al [53] and Sun et al [85]. While Askari et al [11] and Askari and Montazerin [10] introduced forecasting models used fuzzy clustering algorithms, Wang and [92] presented an approach based on automatic clustering. Cheng et al [41] utilized the K-means clustering algorithm to cluster subscripts of the fuzzy sets.…”

Section: Literature Reviewmentioning

confidence: 99%

A combined robust fuzzy time series method for prediction of time series

Yolcu

Lam

2017

Neurocomputing

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In case of outlier(s) it is inevitable that the performance of the fuzzy time series prediction methods is influenced adversely. Therefore, current prediction methods will not be able to provide satisfactory accuracy rates for defuzzified outputs (predictions) when the data has outlier(s). In this study, not only to be able to sort out this problem but also to be able to improve the forecasting accuracy, we propose a combined robust approach for fuzzy time series by assessing how the prediction performance of the methods will be affected from the

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Section: Literature Reviewmentioning

confidence: 99%

A combined robust fuzzy time series method for prediction of time series

Yolcu

Lam

2017

Neurocomputing

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“…Alguns trabalhos utilizam técnicas de clusterização diretamente para a fuzzificação do histórico, dispensando a criação de intervalos no universo de discurso [98,84,99,100]. Askari et al [100] relatam que esta abordagem de clusterização, que dispensa o uso de intervalos, é preferível à abordagem que busca obter a melhor partição do universo de discurso.…”

Section: Tamanho Do Intervalo Dos Universos De Discursounclassified

“…Askari et al [100] relatam que esta abordagem de clusterização, que dispensa o uso de intervalos, é preferível à abordagem que busca obter a melhor partição do universo de discurso. Outras técnicas para a partição incluem otimização a uma variável e informação granular, entre outros.…”

Section: Tamanho Do Intervalo Dos Universos De Discursounclassified

“…A partição do universo de discurso, para obtenção de intervalos ótimos, continua a ser um problema em aberto na literatura [100]. A Tabela 2.3 apresenta um resumo das técnicas encontradas na literatura, que foram utilizadas para partição do universo de discurso em intervalos.…”

Section: Tamanho Do Intervalo Dos Universos De Discursounclassified

“…A maioria dos artigos da literatura FTS não realiza previsão: estima os próprios dados utilizados para estabelecer as FLRs -o que pode ser relacionado a um problema de interpolação ou aproximação, e não a um problema de previsão 7 [127,7]. Contornando este equívoco, mais recentemente, os autores passaram a tratar do problema de previsão com os modelos FTS (e.g., [7,100,112]). …”

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Previsões Pontuais E Intervalares De Séries Temporais De Alta Frequência Com Sistema De Lógica Fuzzy

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Enhanced oil recovery assignment using a new strategy for clustering oil reservoirs: Application of fuzzy logics

Khojastehmehr

Naderifar

Aminshahidy

2018

Journal of Chemometrics

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Enhanced Oil Recovery (EOR) aims at producing more oil from low productive reservoirs. EOR is a costly process due to the time and expenses required to allocate a suitable EOR method to an oil reservoir. For this purpose, this work presents a systematic approach to incorporate past experiences into a compact system to assign the EOR process to oil reservoirs. Because the number of clusters within the reservoirs data is not known, the method proposes a novel validity index to calculate optimal number of clusters copt. Then, the data are clustered to coptcompact clusters according to their similarities, dissimilarities, rock, and fluid properties using fuzzy clustering algorithm. To decide on the EOR process to be applied to a new reservoir, this reservoir is assigned to one of the copt clusters based on similarity, and then the EOR method is chosen based on that cluster. This approach is used for available reservoirs data from different oil fields, and its effectiveness and superiority over previous methods are confirmed.

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A clustering based forecasting algorithm for multivariable fuzzy time series using linear combinations of independent variables

Cited by 58 publications

References 31 publications

A combined robust fuzzy time series method for prediction of time series

A combined robust fuzzy time series method for prediction of time series

Previsões Pontuais E Intervalares De Séries Temporais De Alta Frequência Com Sistema De Lógica Fuzzy

Enhanced oil recovery assignment using a new strategy for clustering oil reservoirs: Application of fuzzy logics

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