长短期记忆网络(LSTM)是一种用于深度学习领域的人工回归神经网络(RNN)结构。与标准的前馈神经网络不同,LSTM具有反馈连接。它不仅可以处理单个数据点(如图像),还可以处理整个数据序列(如语音或视频)。例如,LSTM适用于未分段、连接的手写识别、语音识别、网络流量或IDSs(入侵检测系统)中的异常检测等任务。

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The landscape of city-wide mobility behaviour has altered significantly over the past 18 months. The ability to make accurate and reliable predictions on such behaviour has likewise changed drastically with COVID-19 measures impacting how populations across the world interact with the different facets of mobility. This raises the question: "How does one use an abundance of pre-covid mobility data to make predictions on future behaviour in a present/post-covid environment?" This paper seeks to address this question by introducing an approach for traffic frame prediction using a lightweight Dual-Encoding U-Net built using only 12 Convolutional layers that incorporates a novel approach to skip-connections between Convolutional LSTM layers. This approach combined with an intuitive handling of training data can model both a temporal and spatio-temporal domain shift (gitlab.com/alchera/alchera-traffic4cast-2021).

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