Abstract
This paper presents a mechanism that integrates a grey model (GM) with an exponentially weighted moving average adjustor, known as the EGM. This novel EGM(1,1) model is combined with Markov processes to give the MEGM(1,1) model, which is compared with the original GM(1,1) by exploring the precision of their forecasts. The experimental results show that the average precision of MEGM(1,1) using theoretical models improves from 21.00% to 64.72%, whereas for the empirical models it improves from 40.65% to 96.93%, effectively reducing the error level of the GM(1,1) model.
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