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There is no singular best set of hyperparameters. Even more, there is no real search algorithm for hyperparameters. You can do a grid search, but this obviously will take some time. Most people either do that or will try to handpick their parameters. A few other things to note: Initializing your weights at [10^9,10^8] doesn't seem right to me. They should be ...


I would say it is the nature of data. Generally speaking, you are trying to predict a random sequence, especially if you use the history data as an input and try to get the future value as an output.


If you used your five $X_{test}$ sets multiple times (to measure the average AUC) to decide on the best set of hyperparameters (i.e. optimizer, learning rate, batch size, dropout, activation) then yes, you successfully conducted hyper-parameter optimization. However, the AUC you received for the best set of hyperparameters found (by manual tuning) is not ...

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