A neural network-based fuzzy time series model to improve forecasting

Yu, Tiffany Hui‐Kuang; Huarng, Kun‐Huang

doi:10.1016/j.eswa.2009.10.013

Cited by 156 publications

(59 citation statements)

References 55 publications

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“…Cheng et al [41] utilized the K-means clustering algorithm to cluster subscripts of the fuzzy sets. [100,101] used forecasting models which consider all membership values and determine these values subjectively in order to cope with the loss of information and negative effects of this situation on the performance. Alpaslan and Cagcag [7], Alpaslan et al [8], and Yolcu et al [98] used FCM technique instead of determining the membership values subjectively.…”

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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“…In recent years, several methods have been proposed to model and forecast product demand, such as Bayesian Model (Lee, Lee & Lee, 2012;Pezzulli et al, 2006), Neural Network (Tilokavichai & Sophatsathit, 2011;Lau, Ho & Zhao, 2013;Singhal & Swarup, 2011;Yu & Huarng, 2010), Times Series Analysis (Box, Jenkins & Reinsel, 2013;Hunt & Amarawickrama, 2008;Zhang, Huang & Zhao, 2013), Grey Theory (Yao, Chi & Chen, 2003;Hsu & Chen, 2003) Genetic Algorithm (Haldenbilen & Ceylan, 2005) and so on. Exponential Smoothing Method (ESM) is often considered to apply for building forecasting model, which is developed by Robert G. Brown (Brown & Meyer, 1961).…”

Section: Research Framework and Purposesmentioning

confidence: 99%

Research on industrialization of electric vehicles with its demand forecast using exponential smoothing method

Peng

Zhang

Wang

et al. 2014

JIEM

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“…The general approach to forecasting involves fuzzification of the time series, establishing fuzzy relations and defuzzification (Yu and Huarng, 2010). Fuzzification involves partitioning of the data into fuzzy sets.…”

Section: Time Series Analysismentioning

confidence: 99%

A unified model for context-based behavioural modelling and classification

Dabrowski

Villiers

2015

Expert Systems with Applications

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A unified Bayesian model that simultaneously performs behavioural modelling, information fusion and classification is presented. The model is expressed in the form of a dynamic Bayesian network (DBN). Behavioural modelling is performed by tracking the continuous dynamics of a entity and incorporating various contextual elements that influence behaviour. The entity is classified according to its behaviour. Classification is expressed as a conditional probability of the entity class given its tracked trajectory and the contextual elements. Inference in the DBN is performed using a derived Gaussian sum filter. The model is applied to classify vessels, according to their behaviour, in a maritime piracy situation. The novel aspects of this work include the unified approach to behaviour modelling and classification, the way in which contextual information is fused, the unique approach to classification according to behaviour and the associated derived Gaussian sum filter inference algorithm.

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A neural network-based fuzzy time series model to improve forecasting

Cited by 156 publications

References 55 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

Research on industrialization of electric vehicles with its demand forecast using exponential smoothing method

A unified model for context-based behavioural modelling and classification

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