Bitcoin price prediction using LSTM, GRU and hybrid LSTM-GRU with bayesian optimization, random search, and grid search for the next days

Bitcoin has high price fluctuations, which involve high risks and high return rates for investors.These high earnings have attracted the attention of investors.This paper proposes a new model for Bitcoin price prediction that effectively reduces prediction error.Hyperparameter optimization methods such as Bayesian optimization (BO), random search and grid search with Long Short-Term Memory (LSTM), Gated Repetitive Unit (GRU), and hybrid LSTM-GRU utilised.Models with BO achieved better results than others.To improve each model's results with BO; Gradient Incremental Regression Trees (GBRT), Gaussian Process (GP), Random Forest (RF) and Extra Trees (ET) were applied to optimizers and corresponding surrogate functions.Evaluating the effects of hyper-parameter values on the problem for each method contributes to the parameter selection process for similar prediction problems.To increase comparability in the literature, Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and Mean Square Error (MSE) were used.There is a least one hyper-parameter combination, which produces a result close to the best value for each model when the results obtained from the experiments are interpreted.BO with hybrid LSTM-GRU outperformed all methods in this paper and the examined literature for the value of RMSE, MSE, and MAE.