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首页> 外文期刊>Wireless Communications Letters, IEEE >Time-Wise Attention Aided Convolutional Neural Network for Data-Driven Cellular Traffic Prediction
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Time-Wise Attention Aided Convolutional Neural Network for Data-Driven Cellular Traffic Prediction

机译：用于数据驱动的蜂窝交通预测的辅助卷积神经网络

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摘要

Recurrent neural network (RNN) based models are widely adopted to capture temporal dependencies in the state-of-the-art approaches for cellular traffic prediction. However, RNN is inefficient and incapable of capturing long-range temporal dependencies of traffic data. Besides, its inherent sequential nature makes it time consuming in capture the temporal dependencies. To better capture the long-term temporal dependency and reduce the consumed time in traffic data prediction, we propose a time-wise attention aided convolutional neural network (TWACNet) structure for cellular traffic prediction. In the proposed TWACNet, the time-wise attention mechanism is adopted to capture long-range temporal dependencies of the cellular traffic data and the convolutional neural network (CNN) is adopted to capture the spatial correlation. The performance of TWACNet in traffic prediction is tested in real-world cellular traffic datasets. Experimental results demonstrate that our proposed approach can considerably outperform those existing prediction methods in terms of root mean square errors (RMSE) and training time.
机译：基于蜂窝流量预测的最先进方法中的经常性神经网络（RNN）基于基于神经网络（RNN）的模型被广泛采用了捕获最新方法的时间依赖性。但是，RNN效率低，无法捕获交通数据的远程时间依赖性。此外，其固有的顺序性质使得捕获时间依赖性耗时。为了更好地捕获长期的时间依赖性并减少流量数据预测中的消耗时间，我们提出了一种用于蜂窝交通预测的辅助辅助神经网络（TWACNet）结构。在所提出的Twack中，采用时间明智的注意机制来捕获蜂窝交通数据的远程时间依赖性，采用卷积神经网络（CNN）来捕获空间相关性。在现实世界蜂窝流量数据集中测试了TWACNET在流量预测中的性能。实验结果表明，我们所提出的方法可以在根均线误差（RMSE）和训练时间方面大大差异。

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