A transformer autoencoder with local attention learns discriminative latent features from sparse irregular time series, yielding more consistent risk estimates for electricity theft than standard methods.
In: 2019 IEEE International Conference on Advanced Trends in Information Theory (ATIT), pp
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Transformer autoencoder with local attention for sparse and irregular time series with application on risk estimation
A transformer autoencoder with local attention learns discriminative latent features from sparse irregular time series, yielding more consistent risk estimates for electricity theft than standard methods.