Nonstationary Data Prediction Model Using Grey Time Series Method

Paul Boye, Kofi Agyarko


Researchers are constantly faced with the problem of stationarizing dataset in order to meet time series modelling assumptions. Some methods like autoregressive fractionally integrated moving average (ARFIMA) have been proposed in literature which considers that the differencing of the dataset can take fractional as well as negative values to stationarize the dataset. However, this procedure is time consuming and based on trial and error. Consequently, by means of addressing this problem, a novel GM (1, 1) model is proposed for nonstationary Housing Unit Price (HUP) time series dataset. This technique eliminates the stress of satisfying modelling assumptions of stationarizing the dataset for the model development. The proposed model performance was assessed based on the percentage improvement of the following reliable statistical performance indicators: mean absolute percentage error (MAPE), normalized root mean square error (NRMSE) and relative percentage error (RPE). The results of the study are considered to be efficient and good. As a result, the proposed grey model is considered as a promising nonstationary time series model which can be applied in science to meet the needs of researchers as well as the housing industry to benefit both sellers and prospective buyers for a timely housing unit price prediction.


Grey System Theory;Nonstationary Data;Residual Analysis;Time Series Analysis

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