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CoMemNet: A Continual Memory Network with Drift-Aware Sampling for Traffic Prediction
Traffic sensor networks evolve as sensors are added and traffic distributions change, whereas most forecasting models assume a fixed node set and repeatedly retrain on all available data. We propose CoMemNet, a Continual Memory Network for efficient prediction over evolving traffic sensor networks.
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