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. 2022 May 9;22(9):3592. doi: 10.3390/s22093592
Algorithm 2 Training Process
Input:
 y: bandwidth slice, p: Epochs, B: Batch size, X: training, g: testing, α: learning rate,  ϑ^: Initial Model
 Output:
 ϑ: LSTM Model, E: Forecast Error, P: parameters
 Process:
 01: begin
 02: for I ← to p
 03: ϑ ← forward propagate ( ϑ^, X, y, α,B)
 04: E← Backward Propagation (ϑ,g)
 05: P←Updateϑ,E,α
 06: End for