A novel procedure for multimodel development using the grey silhouette coefficient for small-data-set forecasting

Chang, Che-Jung; Dai, Wen Li; Chen, Chien Chih

doi:10.1057/jors.2015.17

Cited by 14 publications

(8 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where a is the mean intra-cluster distance and b the mean nearest-cluster distance, 24 a distance between a sample and its nearest neighbor cluster. The larger the value of SS, the better the clustering effect.…”

Section: The Second Stage-clustering and Verificationmentioning

confidence: 99%

Two-stage turnout fault diagnosis based on similarity function and fuzzy c-means

Huang

Yang

Wang

et al. 2018

Advances in Mechanical Engineering

View full text Add to dashboard Cite

Fault diagnosis for turnouts is crucial to the safety of railways. Existing studies on fault diagnosis depend on human experiences to select reference curves and require fault type information beforehand. Therefore, we proposed a turnout fault diagnosis method, named similarity function and fuzzy c-means based two-stage algorithm to detect faults and identify fault types in real time. First, the reference curve is selected from current curves representing turnout actions by K-means algorithm; then, a similarity function called Fréchet distance is used to distinguish normal and abnormal curves. Second, an improved fuzzy c-means algorithm is employed to cluster curves automatically. To be more specific, it can double-confirm the normal curves recognized in the first step as well as divide the abnormal curves into different types. Furthermore, possible causes for each fault type are inferred according to their curves. Our approach integrates fault detection and fault classification into one model and would better help the diagnosis of turnouts. The analysis results based on the similarity function and fuzzy c-means based two-stage algorithm algorithm indicate that the analyzed turnout fault types can be diagnosed automatically with high accuracy. Furthermore, since the proposed similarity function and fuzzy c-means algorithm does not need to know fault types in advance, it is applicable in identifying new fault types.

show abstract

Section: The Second Stage-clustering and Verificationmentioning

confidence: 99%

Two-stage turnout fault diagnosis based on similarity function and fuzzy c-means

Huang

Yang

Wang

et al. 2018

Advances in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

“…The authors argue that the optimised model significantly improves the precision and forecasting performance of the original NGBM (1, 1). Chang et al (2015) provide some evidence regarding the problems of small data set forecasting, particularly in manufacturing system, and indicate that the forecasting errors and results with limited data set could be improved by using a multi-model procedure (grey incidence analysis and hybrid forecasting model).…”

Section: Theoretical Framework Literature Review and Hypothesis Devementioning

confidence: 99%

Audit report forecast: an application of nonlinear grey Bernoulli model

Salehi

Dehnavi

2018

View full text Add to dashboard Cite

Purpose-The widespread application of traditional grey model (GM) in different academic fields such as electrical engineering, education, mechanical engineering and agriculture provided the authors with an incentive to conduct the present empirical research in an accounting field, in particular, auditing practice. In this regard, the purpose of this paper is to employ the nonlinear type of the original GM to forecast the drastically changed data on audit reports, primarily due to the fact that the linear nature of GM is unable to forecast nonlinear data precisely. In essence, this paper adds value to the strand of audit report literature by examining the impact of different financial ratios on auditors' opinion and then forecasting audit reports by employing GMs. Design/methodology/approach-The grey forecasting model is known as a system containing uncertain information presented by grey numbers, equations and matrices. The grey forecasting model is employed by using a differential equation in an uncertain system with limited data set which is suitable for smoothing discrete data. In addition, the analyses are conducted by applying a sample of top 50 listed companies on the Tehran Stock Exchange during 2011-2016. Findings-The findings suggest that audit reports are most influenced by the current ratio and conversely, least influenced by the ratio of working capital turnover. Moreover, the authors argue that the Nash nonlinear grey Bernoulli model is more precise than the nonlinear grey Bernoulli model and GM in forecasting audit reports. Originality/value-The current study may give more strength to stakeholders in order to analyse and forecast audit report.

show abstract

“…Salmeron proposed an autonomous FGCM-based system for surveillance asset coordination [21]. Chang used the grey silhouette coefficient to build a novel procedure for multimodel development [22]. Aydemir developed an EPQ model by degree of greyness approach [23].…”

Section: Introductionmentioning

confidence: 99%

A Novel Fractional-Order Grey Prediction Model and Its Modeling Error Analysis

Meng

Zeng

2019

Information

View full text Add to dashboard Cite

Based on the grey prediction model GM(1,1), a novel fractional-order grey prediction model is proposed and its modeling error is systematically studied. In this paper, exponential data sequences are generated for numerical simulation. Via the numerical simulation method, the mean absolute percentage error (MAPE) of the fractional-order GM(1,1) with different values of order and development coefficient is compared to the GM(1,1) and the discrete GM(1,1). The error distribution of the sequences of exponential data is given. The GM(1,1) and the direct modeling GM(1,1) are both special cases of the fractional-order GM(1,1). The conclusion is helpful to further optimize the grey model using fractional-order operators and to expand the applicable bound of GM(1,1).

show abstract

A novel procedure for multimodel development using the grey silhouette coefficient for small-data-set forecasting

Cited by 14 publications

References 29 publications

Two-stage turnout fault diagnosis based on similarity function and fuzzy c-means

Two-stage turnout fault diagnosis based on similarity function and fuzzy c-means

Audit report forecast: an application of nonlinear grey Bernoulli model

A Novel Fractional-Order Grey Prediction Model and Its Modeling Error Analysis

Contact Info

Product

Resources

About