Performance Evaluation of Feature Encoding Methods in Network Traffic Prediction Using Recurrent Neural Networks

Recurrent neural network method considering traffic volume, timestamp and day of the week (RNN-VTD method) is a promising network traffic prediction method because of its high prediction accuracy. The RNN-VTD method encodes timestamp and day of the week, which are categorical data, to numerical data using label encoding. The label encoding, however, gives magnitude to the encoded values, which may cause misunderstanding of recurrent neural network models, and consequently, the prediction accuracy of the RNN-VTD method may be degraded. In this paper, we investigate the effect of using one-hot encoding instead of label encoding for a feature encoding method in the RNN-VTD method. In the one-hot encoding, each input data is encoded to an k-dimensional 0-1 vector where k is the number of category types. Because the encoded data do not have magnitude, it is expected that the prediction accuracy of the RNN-VTD method is improved.