STTS-EAD embeds anomaly detection into the training loop of a spatio-temporal forecaster and reports 3.8 to 8.1 percent RMSE gains over baselines.
Domain fusion cnn-lstm for short-term power consumption forecasting,
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STTS-EAD: Improving Spatio-Temporal Learning Based Time Series Prediction via
STTS-EAD embeds anomaly detection into the training loop of a spatio-temporal forecaster and reports 3.8 to 8.1 percent RMSE gains over baselines.