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目的探讨GM(1,1,sinω)模型在肾综合征出血热(HFRS)发病率预测的应用。方法利用1984～2004年沈阳市HFRS发病率资料建立GM(1,1)预测模型和GM(1,1,sinω)预测模型,对样本进行拟合和预测并对两者的拟合和预测效果进行比较。结果GM(1,1)预测模型为XΣ(1)(k+1)=-541.5277e-0.0092k+551.4778;GM(1,1,sinω)模型为XΣω(1)(k+1)=-158.4104e-0.0444k+162.6622+11.7276sin2kπ/21+5.6982cos2kπ/21,GM(1,1,sinω)模型拟合精度较好(C=0.3912,P=0.9048)。GM(1,1)和GM(1,1,sinω)预测模型拟合的平均误差率(MER)分别为50.22%、20.34%;两者的预测MER分别为25.64%、13.10%,无论从拟合效果还是从预测效果来看GM(1,1,sinω)模型xing1,sinω)forecast的MER均低于GM(1,1)模型。结论GM(1,1,sinω)模型克服了传统灰色模型GM(1,1)的局限性,对于波动性较大且具有周期性的资料具有很好的实用价值。
Objective To investigate the application of the GM (1,1, sinω) model in predicting the incidence of hemorrhagic fever with renal syndrome (HFRS). Methods The GM (1,1) prediction model and the GM (1,1, sinω) prediction model were established by using the incidence of HFRS in Shenyang from 1984 to 2004 to fit and predict the samples and to fit and predict the results. Compare. Results The GM (1,1) prediction model is XΣ (1) (k + 1) = - 541.5277e-0.0092k + 551.4778; The fitting accuracy of 158.4104e-0.0444k + 162.6622 + 11.7276sin2kπ / 21 + 5.6982cos2kπ / 21 and GM (1,1, sinω) was better (C = 0.3912, P = 0.9048). The MERs of the GM (1,1) and GM (1,1, sinω) models were 50.22% and 20.34%, respectively. The MERs of the two models were 25.64% and 13.10% respectively, The synergistic effect is also that the MERs of the GM (1,1, sinω) models xing1 and sinω) forecasts are all lower than the GM (1,1) models from the forecast effect. Conclusion The GM (1,1, sinω) model overcomes the limitations of the traditional gray model GM (1,1) and has good practical value for the data with large volatility and periodicity.