A quantum autoencoder for multivariate time series anomaly detection achieves competitive performance with neural-network autoencoders using fewer trainable parameters.
A ma- chine learning approach for forecasting hierarchical time series,
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Quantum Autoencoder for Multivariate Time Series Anomaly Detection
A quantum autoencoder for multivariate time series anomaly detection achieves competitive performance with neural-network autoencoders using fewer trainable parameters.