A model for real-time failure prognosis based on hidden Markov model and belief rule base

Zhou, Zhi Jie; Hu, Chang Hua; Xu, Dong-Ling; Chen, Mao Yin; Zhou, Dong

doi:10.1016/j.ejor.2010.03.032

Cited by 119 publications

(46 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For recent surveys on the application of RSMs in empirical finance, we refer to Lange andRahbek (2009) andGuidolin (2011). These models have broader fields of application, covering manpower systems, where both observable and latent sources of dynamic heterogeneity should be accounted for (Guerry, 2011), and reliability analysis (Zhou et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Clustering financial time series: New insights from an extended hidden Markov model

Dias

Vermunt

Ramos

2015

European Journal of Operational Research

View full text Add to dashboard Cite

In recent years, large amounts of financial data have become available for analysis. We propose exploring returns from 21 European stock markets by model-based clustering of regime switching models. These econometric models identify clusters of time series with similar dynamic patterns and moreover allow relaxing assumptions of existing approaches, such as the assumption of conditional Gaussian returns. The proposed model handles simultaneously the heterogeneity across stock markets and over time, i.e., time-constant and timevarying discrete latent variables capture unobserved heterogeneity between and within stock markets, respectively. The results show a clear distinction between two groups of stock markets, each one characterized by different regime switching dynamics that correspond to different expected return-risk patterns. We identify three regimes: the so-called bull and bear regimes, as well as a stable regime with returns close to 0, which turns out to be the most frequently occurring regime. This is consistent with stylized facts in financial econometrics.

show abstract

Section: Introductionmentioning

confidence: 99%

Clustering financial time series: New insights from an extended hidden Markov model

Dias

Vermunt

Ramos

2015

European Journal of Operational Research

View full text Add to dashboard Cite

show abstract

“…In the data-driven based method, the observable data and the corresponding methods are used directly to establish the forecasting model the characteristic value that can reflect the hidden behavior. The data-driven method includes hidden Markov model (HMM) based method [5][6][7][8], grey theory based method [9], dynamic Bayesian network (DBN) based method [10,11], and Wavelet neural network (WNN) based method [12]. The qualitative knowledge based method that includes expert system based model [13], Petri net based model [14] and so on can be adopted to establish the forecasting model of the hidden behavior.…”

Section: Q3mentioning

confidence: 99%

A method for predicting the network security situation based on hidden BRB model and revised CMA-ES algorithm

Zhou

Zhang

et al. 2016

Applied Soft Computing

Self Cite

View full text Add to dashboard Cite

“…The initial covariance matrix is a symmetric, and positive definite matrix: (6) where B denotes an orthogonal matrix, D denotes a diagonal matrix with square roots of eigenvalues of C as diagonal elements. The new covariance matrix in next generation is: is updated by Eq.…”

Section: Adapting the Covariance Matrixmentioning

confidence: 99%

“…In order to predict the network security situation, many methods have been proposed, such as HMM (hidden Markov model) [3][4][5][6], and the neural network [7]. All of these forecasting model need the parameter optimization in order to achieve proper effect, and its parameter optimization has the following characteristics:…”

Section: Introductionmentioning

confidence: 99%

An Efficient Improvement of CMA-ES Algorithm for the Network Security Situation Prediction

Hu¹,

Pei-li²

2015

TOAUTOCJ

View full text Add to dashboard Cite

An improved covariance matrix adaptation evolution strategy algorithm (CMA-ES) is proposed and it is used to train the forecasting model of the network security situation in this paper. A new recombination strategy which adds a heuristic component is developed in the improved CMA-ES algorithm, and the search speed is accelerated. The experimental results show that, compare with original algorithm and its variants, the improved CMA-ES algorithm can greatly increased the search speed in high dimensional problems. The improved CMA-ES algorithm is an efficient evolutionary algorithm which can be applied to the network security situation prediction.

show abstract

A model for real-time failure prognosis based on hidden Markov model and belief rule base

Cited by 119 publications

References 39 publications

Clustering financial time series: New insights from an extended hidden Markov model

Clustering financial time series: New insights from an extended hidden Markov model

A method for predicting the network security situation based on hidden BRB model and revised CMA-ES algorithm

An Efficient Improvement of CMA-ES Algorithm for the Network Security Situation Prediction

Contact Info

Product

Resources

About