Inspired by the recent success of deep learning in multiscale information
encoding, we introduce a variational autoencoder (VAE) based semi-supervised
method for detection of faulty traffic data, which is cast as a classification
problem. Continuous wavelet transform (CWT) is applied to the time series of
traffic volume data to obtain rich features embodied in time-frequency
representation, followed by a twin of VAE models to separately encode normal
data and faulty data. The resulting multiscale dual encodings are concatenated
and fed to an attention-based classifier, consisting of a self-attention module
and a multilayer perceptron. For comparison, the proposed architecture is
evaluated against five different encoding schemes, including (1) VAE with only
normal data encoding, (2) VAE with only faulty data encoding, (3) VAE with both
normal and faulty data encodings, but without attention module in the
classifier, (4) siamese encoding, and (5) cross-vision transformer (CViT)
encoding. The first four encoding schemes adopted the same convolutional neural
network (CNN) architecture while the fifth encoding scheme follows the
transformer architecture of CViT. Our experiments show that the proposed
architecture with the dual encoding scheme, coupled with attention module,
outperforms other encoding schemes and results in classification accuracy of
96.4%, precision of 95.5%, and recall of 97.7%.Comment: 16 pages, 8 figure