Predicting Wavelet-Transformed Stock Prices Using a Vanishing Gradient Resilient Optimized Gated Recurrent Unit with a Time Lag

Abstract

The development of accurate prediction models continues to be highly beneficial in myriad disciplines. Deep learning models have performed well in

stock price prediction and give high accuracy. However, these models are

largely affected by the vanishing gradient problem escalated by some activation functions. This study proposes the use of the Vanishing Gradient Resilient Optimized Gated Recu rrent Unit (OGRU) model with a scaled mean

Approximation Coefficient (AC) time lag which should counter slow convergence, vanishing gradient and large error metrics. This study employed the

Rectified Linear Unit (ReLU), Hyperbolic Tangent (Tanh), Sigmoid and Exponential Linear Unit (ELU) activation functions. Real-life datasets

including

the daily Apple and 5-minute Netflix closing stock prices were used, and they

were decomposed using the Stationary Wavelet Transform (SWT). The decomposed series formed a decomposed data

model which was compared to

an undecomposed data

model with similar hyperparameters and different default lags. The Apple daily dataset performed well with a Default_1 lag, using

an undecomposed data model and the ReLU, attaining 0.01312, 0. 00854 and 3.67 minutes for RMSE, MAE and runtime. The Netflix data performed best with the MeanAC_42 lag, using decomposed data model and the ELU achieving 0.00620, 0.00487 and 3.01 minutes for the same metrics.