Accurate real-time prediction of thrust and torque is crucial for tunnel boring machines(TBM)tunneling safety and efficiency.This paper proposes Att-Wave-ConvNet(AWCNet),a TBM thrust and torque prediction model based ...Accurate real-time prediction of thrust and torque is crucial for tunnel boring machines(TBM)tunneling safety and efficiency.This paper proposes Att-Wave-ConvNet(AWCNet),a TBM thrust and torque prediction model based on TimesNet,designed for longer real-time forecasting.It replaces fast Fourier transform and complex convolution with discrete wavelet packet transform and two-dimensional elevated convolution,while integrating a multi-head self-attention mechanism to enhance long-sequence feature extraction.Experiments on field data from a challenging hard-rock tunneling project show that AWCNet achieves reliable prediction even at a 60-s length,outperforming TimesNet,Dlinear,Informer and Transformer.Compared to existing methods,it reduces lowest root mean square error(RMSE)and mean absolute error(MAE)by 0.169 and 0.047,improves R~2 by 4.8%,and shortens prediction runtime by 26.07%.The results confirm that AWCNet ensures both accuracy and efficiency for real-time TBM load prediction,demonstrating its practical applicability over extended durations.展开更多
时间序列预测是金融领域的重要研究课题,对提高决策效率和风险管理具有重要意义。本文提出并构建了多种基于Transformer架构的时间序列预测模型,包括Vanilla Transformer、Informer、FEDformer、Autoformer、PatchTST和TimesNet,并以浦...时间序列预测是金融领域的重要研究课题,对提高决策效率和风险管理具有重要意义。本文提出并构建了多种基于Transformer架构的时间序列预测模型,包括Vanilla Transformer、Informer、FEDformer、Autoformer、PatchTST和TimesNet,并以浦发银行股票收盘价作为预测对象,探讨了单变量和多变量输入下这些模型的预测性能。实验结果表明,基于Transformer架构的模型在预测精度和稳定性方面显著优于传统模型(ARIMA和VAR)。其中Vanilla Transformer在单变量预测中表现最佳(MSE = 0.0455, MAE = 0.1480),而TimesNet在多变量预测中性能最优(MSE = 0.0463, MAE = 0.1513)。研究表明,基于Transformer的模型在处理复杂时间序列问题时具有显著优势,为金融时间序列预测提供了新的方法和参考。未来可进一步探索Transformer与其他深度学习技术的结合,以提升模型的泛化能力和实用性。Time series forecasting is an important research topic in the financial field, with significant implications for improving decision-making efficiency and risk management. This paper proposes and constructs several time series forecasting models based on the Transformer architecture, including Vanilla Transformer, Informer, FEDformer, Autoformer, PatchTST, and TimesNet. Using the closing price of Shanghai Pudong Development Bank stock as the forecasting target, the paper examines the predictive performance of these models under univariate and multivariate inputs. Experimental results show that Transformer-based models significantly outperform traditional models (ARIMA and VAR) in terms of prediction accuracy and stability. Among them, the Vanilla Transformer performs the best in univariate forecasting (MSE = 0.0455, MAE = 0.1480), while TimesNet has the best performance in multivariate forecasting (MSE = 0.0463, MAE = 0.1513). The study demonstrates that Transformer-based models have a significant advantage in handling complex time series problems, providing new methods and references for financial time series forecasting. Future research can further explore the combination of Transformer with other deep learning technologies to enhance the model’s generalization ability and practical utility.展开更多
Telecom network fraud has become the most common and concerning type of crime and is an important public security incident that threatens urban resilience.Therefore,preventing a continuous rise in telecommunications a...Telecom network fraud has become the most common and concerning type of crime and is an important public security incident that threatens urban resilience.Therefore,preventing a continuous rise in telecommunications and network fraud will help establish a resilient urban governance system.Undertaking the spatiotemporal forecasting of telecommunications-network fraud trends is of significant importance for aiding public security agencies in proactive crime prevention and implementing targeted anti-fraud campaigns.This study presents a telecommunication network fraudulent crime prediction method called TSE-Tran,which integrates temporal representation and transformer architecture.The time-series data of telecommunication-network fraud occurrences were input into the TimesNet module,which maps the sequence data to a more precise feature representation tensor that accounts for both intra-and inter-cycle features.Subsequently,the data are fed into the transformer-encoder module for further encoding,capturing long-range dependencies in the time-series data.Finally,occurrences of future telecommunication network frauds are predicted by a fully connected layer.The results of the study demonstrate that the proposed TSE-Tran method outperforms benchmark methods in terms of prediction accuracy.The results of this study are expected to aid in the prevention and control of telecommunications and network frauds effectively strengthen the resilience of urban development and the ability to respond to public security incidents.展开更多
基金supported by the National Key Research and Development Program of China(Grant No.2021YFB3301603)。
文摘Accurate real-time prediction of thrust and torque is crucial for tunnel boring machines(TBM)tunneling safety and efficiency.This paper proposes Att-Wave-ConvNet(AWCNet),a TBM thrust and torque prediction model based on TimesNet,designed for longer real-time forecasting.It replaces fast Fourier transform and complex convolution with discrete wavelet packet transform and two-dimensional elevated convolution,while integrating a multi-head self-attention mechanism to enhance long-sequence feature extraction.Experiments on field data from a challenging hard-rock tunneling project show that AWCNet achieves reliable prediction even at a 60-s length,outperforming TimesNet,Dlinear,Informer and Transformer.Compared to existing methods,it reduces lowest root mean square error(RMSE)and mean absolute error(MAE)by 0.169 and 0.047,improves R~2 by 4.8%,and shortens prediction runtime by 26.07%.The results confirm that AWCNet ensures both accuracy and efficiency for real-time TBM load prediction,demonstrating its practical applicability over extended durations.
文摘时间序列预测是金融领域的重要研究课题,对提高决策效率和风险管理具有重要意义。本文提出并构建了多种基于Transformer架构的时间序列预测模型,包括Vanilla Transformer、Informer、FEDformer、Autoformer、PatchTST和TimesNet,并以浦发银行股票收盘价作为预测对象,探讨了单变量和多变量输入下这些模型的预测性能。实验结果表明,基于Transformer架构的模型在预测精度和稳定性方面显著优于传统模型(ARIMA和VAR)。其中Vanilla Transformer在单变量预测中表现最佳(MSE = 0.0455, MAE = 0.1480),而TimesNet在多变量预测中性能最优(MSE = 0.0463, MAE = 0.1513)。研究表明,基于Transformer的模型在处理复杂时间序列问题时具有显著优势,为金融时间序列预测提供了新的方法和参考。未来可进一步探索Transformer与其他深度学习技术的结合,以提升模型的泛化能力和实用性。Time series forecasting is an important research topic in the financial field, with significant implications for improving decision-making efficiency and risk management. This paper proposes and constructs several time series forecasting models based on the Transformer architecture, including Vanilla Transformer, Informer, FEDformer, Autoformer, PatchTST, and TimesNet. Using the closing price of Shanghai Pudong Development Bank stock as the forecasting target, the paper examines the predictive performance of these models under univariate and multivariate inputs. Experimental results show that Transformer-based models significantly outperform traditional models (ARIMA and VAR) in terms of prediction accuracy and stability. Among them, the Vanilla Transformer performs the best in univariate forecasting (MSE = 0.0455, MAE = 0.1480), while TimesNet has the best performance in multivariate forecasting (MSE = 0.0463, MAE = 0.1513). The study demonstrates that Transformer-based models have a significant advantage in handling complex time series problems, providing new methods and references for financial time series forecasting. Future research can further explore the combination of Transformer with other deep learning technologies to enhance the model’s generalization ability and practical utility.
文摘Telecom network fraud has become the most common and concerning type of crime and is an important public security incident that threatens urban resilience.Therefore,preventing a continuous rise in telecommunications and network fraud will help establish a resilient urban governance system.Undertaking the spatiotemporal forecasting of telecommunications-network fraud trends is of significant importance for aiding public security agencies in proactive crime prevention and implementing targeted anti-fraud campaigns.This study presents a telecommunication network fraudulent crime prediction method called TSE-Tran,which integrates temporal representation and transformer architecture.The time-series data of telecommunication-network fraud occurrences were input into the TimesNet module,which maps the sequence data to a more precise feature representation tensor that accounts for both intra-and inter-cycle features.Subsequently,the data are fed into the transformer-encoder module for further encoding,capturing long-range dependencies in the time-series data.Finally,occurrences of future telecommunication network frauds are predicted by a fully connected layer.The results of the study demonstrate that the proposed TSE-Tran method outperforms benchmark methods in terms of prediction accuracy.The results of this study are expected to aid in the prevention and control of telecommunications and network frauds effectively strengthen the resilience of urban development and the ability to respond to public security incidents.
