Research Article
Evolutionary Framework with Bidirectional Long Short-Term Memory Network for Stock Price Prediction
| | Input: population size , the number of mutations , the batch size , batch data , and initial weight , | | | output: close price of the next day | | (1) | | | (2) | Initializes model parameter : | | (3) | forto | | (4) | save model parameters | | (5) | forto | | (6) | forto | | (7) | assign parameters to the model | | (8) | get a batch as input of EBiLSTM; | | (9) | switch(k) | | (10) | case1: | | (11) | case2: | | (12) | case3: | | (13) | end switch | | (14) | if | | (15) | | | (16) | | | (17) | | | (18) | end for | | (19) | end for | | (20) | end for |
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