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.
Journal of Process Control87, 54–67 (2020)
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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.