An Improved Grey Markov Forecasting Model and Its Application

Li, Juan; Xie, Yong; Zhang, Yilai

doi:10.1109/icdma.2012.46

Cited by 2 publications

(2 citation statements)

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“…The more historical data are used, the more states are needed to divide the sequence. While fewer states cannot reveal the differences between states and cannot adjust to the fluctuations effectively, more states may weaken the law of transition probability (Zhao, 2010;Huang et al, 2011;Juan et al, 2012). Since we have a small sample of data, we divided the errors into four states.…”

Section: The Gm (11) Model and Gm (11) Markov Modelmentioning

confidence: 99%

“…This study reveals that the Grey-Markov model is an efficient technique to increase the forecasting accuracy of grey prediction in cases with exponential and chaotic data. Juan et al (2012) suggested an improved Grey-Markov model, which uses the logarithmic transformation of the original data sequence characterized by large volatility and uses the minimum error rather than a relative error of the grey model to reduce the volatility in the sequence. They compared the results of Grey-Markov with those of the improved version of the model by applying the models to fit and predict Jingdezhen ceramic industrial output from 2003 to 2011.…”

Section: Introductionmentioning

confidence: 99%

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Predicting product demand from small-sized data: grey models

Özdemir

Özdağoğlu

2017

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Purpose Prediction problems raised in uncertain environments require different solution approaches such as grey prediction models, which consider uncertainty in information and also enable the use of small data sets. The purpose of this paper is to investigate the comparative performances of grey prediction models (GM) and Markov chain integrated grey models in a demand prediction problem. Design/methodology/approach The modeling process of grey models is initially described, and then an integrated model called the Grey-Markov model is presented for the convenience of applications. The analyses are conducted on a monthly demand prediction problem to demonstrate the modeling accuracies of the GM (1,1), GM (2,1), GM (1,1)-Markov, and GM (2,1)-Markov models. Findings Numerical results reveal that the Grey-Markov model based on GM (2,1) achieves better prediction performance than the other models. Practical implications It is thought that the methodology and the findings of the study will be a significant reference for both academics and executives who struggle with similar demand prediction problems in their fields of interest. Originality/value The novelty of this study comes from the fact that the GM (2,1)-Markov model has been first used for demand prediction. Furthermore, the GM (2,1)-Markov model represents a relatively new approach, and this is the second paper that addresses the GM (2,1)-Markov model in any area.

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Section: The Gm (11) Model and Gm (11) Markov Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%