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
基于transformer架构,提出一种面向学习轨迹的知识追踪预测模型(knowledge tracing prediction model for learning trajectories,LTKT),解决知识追踪领域使用transformer架构所存在的问题:网络中缺乏知识点信息、注意力被分散到众多关...基于transformer架构,提出一种面向学习轨迹的知识追踪预测模型(knowledge tracing prediction model for learning trajectories,LTKT),解决知识追踪领域使用transformer架构所存在的问题:网络中缺乏知识点信息、注意力被分散到众多关联较小的试题及忽略了学习能力在答题决策中的影响。LTKT在数据预处理阶段,采用教育领域的知识融通机制整合题目涉及的多个知识点,作为模型学习的一个信息维度。在编码器与解码器结构中,根据注意力呈现长尾分布的特点引入稀疏自注意力机制,并在其中嵌入包含绝对距离和相对距离的位置编码,使注意力集中在少数高度相似的试题上,同时加强模型对位置信息的感知。在预测策略上,使用双线性层融合学习能力特征与学生的知识状态,综合预测学生下一时刻的作答表现。在两个真实的大型公开数据集上进行实验,与其他优秀模型进行对比,结果显示LTKT的AUC有了明显提升。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.
文摘基于transformer架构,提出一种面向学习轨迹的知识追踪预测模型(knowledge tracing prediction model for learning trajectories,LTKT),解决知识追踪领域使用transformer架构所存在的问题:网络中缺乏知识点信息、注意力被分散到众多关联较小的试题及忽略了学习能力在答题决策中的影响。LTKT在数据预处理阶段,采用教育领域的知识融通机制整合题目涉及的多个知识点,作为模型学习的一个信息维度。在编码器与解码器结构中,根据注意力呈现长尾分布的特点引入稀疏自注意力机制,并在其中嵌入包含绝对距离和相对距离的位置编码,使注意力集中在少数高度相似的试题上,同时加强模型对位置信息的感知。在预测策略上,使用双线性层融合学习能力特征与学生的知识状态,综合预测学生下一时刻的作答表现。在两个真实的大型公开数据集上进行实验,与其他优秀模型进行对比,结果显示LTKT的AUC有了明显提升。
