The modeling of time series based on fuzzy information granules

Lu, Wei; Pedrycz, Witold; Liu, Xiaodong; Yang, Jianhua; Li, Peng

doi:10.1016/j.eswa.2013.12.005

Cited by 92 publications

(29 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Granular information is often combined with fuzzy time series (Lu et al, 2014;. A general framework for applying machine learning methods for time series prediction is presented in (Wu and Lee, 2015).…”

Section: Time Series Analysismentioning

confidence: 99%

A unified model for context-based behavioural modelling and classification

Dabrowski

Villiers

2015

Expert Systems with Applications

View full text Add to dashboard Cite

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.

show abstract

Section: Time Series Analysismentioning

confidence: 99%

A unified model for context-based behavioural modelling and classification

Dabrowski

Villiers

2015

Expert Systems with Applications

View full text Add to dashboard Cite

show abstract

“…An innovative method for forecasting artificial emotions and designing an affective decision system on the basic of fuzzy cognitive map is proposed in [26]. The application of fuzzy cognitive maps to univariate time series modeling is discussed in [13], [12], [17]. Prediction of work of complex and imprecise systems on the basis of FCM is described in [27].…”

Section: Introductionmentioning

confidence: 99%

Learning Fuzzy Cognitive Maps using Structure Optimization Genetic Algorithm

Poczęta¹,

Yastrebov²,

Papageorgiou³

2015

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

Abstract-Fuzzy cognitive map (FCM) is a soft computing methodology that allows to describe the analyzed problem as a set of nodes (concepts) and connections (links) between them. In this paper a new Structure Optimization Genetic Algorithm (SOGA) for FCMs learning is presented for modeling complex decision support systems. The proposed approach allows to automatic construct and optimize the FCM model on the basis of historical multivariate time series. The SOGA defines a new learning error function with an additional penalty for highly complexity of FCM understood as a large number of concepts and a large number of connections between them. The aim of this study is the analysis of usefulness of the Structure Optimization Genetic Algorithm for fuzzy cognitive maps learning. Comparative analysis of the SOGA with other well-known FCM learning algorithms (RealCoded Genetic Algorithm and Multi-Step Gradient Method) was performed on the example of prediction of rented bikes count. Simulations were done with the ISEMK (Intelligent Expert System based on Cognitive Maps) software tool. The obtained results show that the use of SOGA allows to significantly reduce the structure of the FCM model by selecting the most important concepts and connections between them.

show abstract

“…Based on the nature, FCM has capable to handle prediction problem of time series [15,16]. Further, information granules and information granulation [17,25] play key roles when dealing with large data. As expression of meaningful the abstract entities, information granules come with information granulation.…”

Section: Introductionmentioning

confidence: 99%

Numerical Prediction of Time Series Based on FCMs with Information Granules

Lu¹,

Yang²,

Liu³

2014

INT J COMPUT COMMUN

Self Cite

View full text Add to dashboard Cite

The prediction of time series has been widely applied to many fields such as enrollments, stocks, weather and so on. In this paper, a new prediction method based on fuzzy cognitive map with information granules is proposed, in which fuzzy cmeans clustering algorithm is used to automatically abstract information granules and transform the original time series into granular time series, and subsequently fuzzy cognitive map is used to describe these granular time series and perform prediction. two benchmark time series are used to validate feasibility and effectiveness of proposed method. The experimental results show that the proposed prediction method can reach better prediction accuracy. Additionally, the proposed method is also able to precess the modeling and prediction of large-scale time series.

show abstract

The modeling of time series based on fuzzy information granules

Cited by 92 publications

References 21 publications

A unified model for context-based behavioural modelling and classification

A unified model for context-based behavioural modelling and classification

Learning Fuzzy Cognitive Maps using Structure Optimization Genetic Algorithm

Numerical Prediction of Time Series Based on FCMs with Information Granules

Contact Info

Product

Resources

About