APS-LSTM combines FFT-based multi-period division, periodic and spatial self-attention, and LSTM encoding to improve flood flow forecasts on two real-world watershed datasets.
Modeling and forecasting vehicular traffic flow as a seasonal arima process: Theoretical basis and empirical results,
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APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting
APS-LSTM combines FFT-based multi-period division, periodic and spatial self-attention, and LSTM encoding to improve flood flow forecasts on two real-world watershed datasets.