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
径流过程呈现出的强非线性,使得现有水文模型的预测性能受到制约,深度学习等人工智能方法具有较强的非线性拟合能力,一定程度上可以突破现有瓶颈。为有效提取径流序列的非线性时变特征信息,提高径流模拟精度和多步预测性能,以雅砻江上...径流过程呈现出的强非线性,使得现有水文模型的预测性能受到制约,深度学习等人工智能方法具有较强的非线性拟合能力,一定程度上可以突破现有瓶颈。为有效提取径流序列的非线性时变特征信息,提高径流模拟精度和多步预测性能,以雅砻江上游雅江流域为研究对象,建立了基于具有时变结构的ForecastNet径流预测模型,并与传统水文模型SWAT(Soil and Water Assessnent Teol)和神经网络模型RNN(Recurrent Neural Network)、LSTM(Long Short-Term Memory)及其组合进行对比分析。结果表明,ForcastNet模型在长预见期径流预测中有较强的适用性,能有效提高径流模拟及多步预测精度,为高精度实时径流预测提供了一种技术支撑。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.
文摘径流过程呈现出的强非线性,使得现有水文模型的预测性能受到制约,深度学习等人工智能方法具有较强的非线性拟合能力,一定程度上可以突破现有瓶颈。为有效提取径流序列的非线性时变特征信息,提高径流模拟精度和多步预测性能,以雅砻江上游雅江流域为研究对象,建立了基于具有时变结构的ForecastNet径流预测模型,并与传统水文模型SWAT(Soil and Water Assessnent Teol)和神经网络模型RNN(Recurrent Neural Network)、LSTM(Long Short-Term Memory)及其组合进行对比分析。结果表明,ForcastNet模型在长预见期径流预测中有较强的适用性,能有效提高径流模拟及多步预测精度,为高精度实时径流预测提供了一种技术支撑。
