A new methodology for multi-step-ahead forecasting was proposed herein which combined the wavelet transform(WT), artificial neural network(ANN) and forecasting strategies based on the changing characteristics of avail...A new methodology for multi-step-ahead forecasting was proposed herein which combined the wavelet transform(WT), artificial neural network(ANN) and forecasting strategies based on the changing characteristics of available parking spaces(APS). First, several APS time series were decomposed and reconstituted by the wavelet transform. Then, using an artificial neural network, the following five strategies for multi-step-ahead time series forecasting were used to forecast the reconstructed time series: recursive strategy, direct strategy, multi-input multi-output(MIMO) strategy, DIRMO strategy(a combination of the direct and MIMO strategies), and newly proposed recursive multi-input multi-output(RECMO) strategy which is a combination of the recursive and MIMO strategies. Finally, integrating the predicted results with the reconstructed time series produced the final forecasted available parking spaces. Three findings appear to be consistently supported by the experimental results. First, applying the wavelet transform to multi-step ahead available parking spaces forecasting can effectively improve the forecasting accuracy. Second, the forecasting resulted from the DIRMO and RECMO strategies is more accurate than that of the other strategies. Finally, the RECMO strategy requires less model training time than the DIRMO strategy and consumes the least amount of training time among five forecasting strategies.展开更多
目的探讨中性粒细胞CD64及降钙素原(PCT)预测创伤性脑损伤(TBI)患者预后的价值。方法选取海口市120急救中心收治的TBI患者162例,根据28 d预后情况分成存活组(n=128)和死亡组(n=34),根据格拉斯哥昏迷评分(GCS)分为轻度组(n=106,9分≤GCS...目的探讨中性粒细胞CD64及降钙素原(PCT)预测创伤性脑损伤(TBI)患者预后的价值。方法选取海口市120急救中心收治的TBI患者162例,根据28 d预后情况分成存活组(n=128)和死亡组(n=34),根据格拉斯哥昏迷评分(GCS)分为轻度组(n=106,9分≤GCS≤15分)和重度组(n=56,3分≤GCS≤8分)。比较各组第1、3、5天中性粒细胞CD64及PCT水平变化。应用受试者工作特征(ROC)曲线分析中性粒细胞CD64及PCT水平预测TBI患者预后的价值。采用Pearson相关分析TBI患者中性粒细胞CD64及PCT与GCS评分的相关性。结果死亡组第1、3、5天中性粒细胞CD64(4.15±1.50 vs. 2.40±0.85,6.63±2.10 vs. 3.25±0.96,8.14±2.70 vs. 3.40±0.92)及PCT水平(ng/mL:1.40±0.73 vs. 0.34±0.26,2.35±1.28 vs. 0.64±0.38,5.42±2.16 vs. 0.58±0.34)均明显高于存活组(P<0.05),且死亡组中性粒细胞CD64及PCT水平随时间呈升高趋势(P<0.05)。重度组第1、3、5天中性粒细胞CD64(3.90±1.42 vs. 2.82±0.94,6.12±1.85 vs. 3.60±0.98,7.86±2.64 vs. 3.53±0.87)及PCT水平(ng/mL:1.27±0.65 vs. 0.50±0.32,2.04±1.25 vs. 0.82±0.45,5.16±1.97 vs. 0.73±0.38)均明显高于轻度组(P<0.05),且重度组中性粒细胞CD64及PCT水平随时间呈升高趋势(P<0.05)。ROC曲线显示,第3天中性粒细胞CD64及PCT两项联合预测TBI患者死亡的AUC最大(0.924, 95%CI0.860~0.972),其敏感度和特异度为92.8%和87.2%。相关分析显示,死亡组中性粒细胞CD64及PCT与GCS评分呈负相关(r=-0.752、-0.817,P<0.01),中性粒细胞CD64与PCT呈正相关(r=0.684,P<0.01)。结论中性粒细胞CD64及PCT与TBI患者的病情严重程度相关,第3天两项联合对预测TBI患者预后的价值较高。展开更多
基金Project(51561135003)supported by the International Cooperation and Exchange of the National Natural Science Foundation of ChinaProject(51338003)supported by the Key Project of National Natural Science Foundation of China
文摘A new methodology for multi-step-ahead forecasting was proposed herein which combined the wavelet transform(WT), artificial neural network(ANN) and forecasting strategies based on the changing characteristics of available parking spaces(APS). First, several APS time series were decomposed and reconstituted by the wavelet transform. Then, using an artificial neural network, the following five strategies for multi-step-ahead time series forecasting were used to forecast the reconstructed time series: recursive strategy, direct strategy, multi-input multi-output(MIMO) strategy, DIRMO strategy(a combination of the direct and MIMO strategies), and newly proposed recursive multi-input multi-output(RECMO) strategy which is a combination of the recursive and MIMO strategies. Finally, integrating the predicted results with the reconstructed time series produced the final forecasted available parking spaces. Three findings appear to be consistently supported by the experimental results. First, applying the wavelet transform to multi-step ahead available parking spaces forecasting can effectively improve the forecasting accuracy. Second, the forecasting resulted from the DIRMO and RECMO strategies is more accurate than that of the other strategies. Finally, the RECMO strategy requires less model training time than the DIRMO strategy and consumes the least amount of training time among five forecasting strategies.
文摘目的探讨中性粒细胞CD64及降钙素原(PCT)预测创伤性脑损伤(TBI)患者预后的价值。方法选取海口市120急救中心收治的TBI患者162例,根据28 d预后情况分成存活组(n=128)和死亡组(n=34),根据格拉斯哥昏迷评分(GCS)分为轻度组(n=106,9分≤GCS≤15分)和重度组(n=56,3分≤GCS≤8分)。比较各组第1、3、5天中性粒细胞CD64及PCT水平变化。应用受试者工作特征(ROC)曲线分析中性粒细胞CD64及PCT水平预测TBI患者预后的价值。采用Pearson相关分析TBI患者中性粒细胞CD64及PCT与GCS评分的相关性。结果死亡组第1、3、5天中性粒细胞CD64(4.15±1.50 vs. 2.40±0.85,6.63±2.10 vs. 3.25±0.96,8.14±2.70 vs. 3.40±0.92)及PCT水平(ng/mL:1.40±0.73 vs. 0.34±0.26,2.35±1.28 vs. 0.64±0.38,5.42±2.16 vs. 0.58±0.34)均明显高于存活组(P<0.05),且死亡组中性粒细胞CD64及PCT水平随时间呈升高趋势(P<0.05)。重度组第1、3、5天中性粒细胞CD64(3.90±1.42 vs. 2.82±0.94,6.12±1.85 vs. 3.60±0.98,7.86±2.64 vs. 3.53±0.87)及PCT水平(ng/mL:1.27±0.65 vs. 0.50±0.32,2.04±1.25 vs. 0.82±0.45,5.16±1.97 vs. 0.73±0.38)均明显高于轻度组(P<0.05),且重度组中性粒细胞CD64及PCT水平随时间呈升高趋势(P<0.05)。ROC曲线显示,第3天中性粒细胞CD64及PCT两项联合预测TBI患者死亡的AUC最大(0.924, 95%CI0.860~0.972),其敏感度和特异度为92.8%和87.2%。相关分析显示,死亡组中性粒细胞CD64及PCT与GCS评分呈负相关(r=-0.752、-0.817,P<0.01),中性粒细胞CD64与PCT呈正相关(r=0.684,P<0.01)。结论中性粒细胞CD64及PCT与TBI患者的病情严重程度相关,第3天两项联合对预测TBI患者预后的价值较高。
