Time Series Forecasting Using Optimized Rolling Grey Model
2019
This study attempts to improve the forecasting accuracy of rolling grey model by applying Gaussian bare-bones differential evolution (GBDE) to optimize the weight of background value and number of data points used to construct a rolling-GM(1,1). Experimental results on two real time series forecasting problems show that the proposed GBDE-based rolling-GM(l,l) outperforms the traditional rolling-GM(l,l) in terms of fitting accuracy and forecasting accuracy.
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