Interval grey number sequence prediction by using non-homogenous exponential discrete grey forecasting model

Naiming Xie · Sifeng Liu

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Grey System Theory Applications Forecasting Techniques and Applications Fuzzy Systems and Optimization

Abstract

This paper aims to study a new grey prediction approach and its solution for forecasting the main system variable whose accurate value could not be collected while the potential value set could be defined. Based on the traditional non-homogenous discrete grey forecasting model (NDGM), the interval grey number and its algebra operations are redefined and combined with the NDGM model to construct a new interval grey number sequence prediction approach. The solving principle of the model is analyzed, the new accuracy evaluation indices, i.e. mean absolute percentage error of mean value sequence (MAPEM) and mean percent of interval sequence simulating value set covered (MPSVSC), are defined and, the procedure of the interval grey number sequence based the NDGM (IG-NDGM) is given out. Finally, a numerical case is used to test the modelling accuracy of the proposed model. Results show that the proposed approach could solve the interval grey number sequence prediction problem and it is much better than the traditional DGM(1,1) model and GM(1,1) model.

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References

2005Grey Information: Theory and Practical Applications 2008Discrete grey forecasting model and its optimization 2007Grey prediction with rolling mechanism for electricity demand forecasting of Turkey 2010Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India 2006Forecasting of foreign exchange rates of Taiwan’s major trading partners by novel nonlinear Grey Bernoulli model NGBM(1,1)2009Multi-Attribute Decision-Making Model by Applying Grey Numbers 2001Applied Hybrid Grey Model to Forecast Seasonal Time Series 2012On novel grey forecasting model based on non-homogeneous index sequence

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