TITLE:
Predicting Wavelet-Transformed Stock Prices Using a Vanishing Gradient Resilient Optimized Gated Recurrent Unit with a Time Lag
AUTHORS:
Luyandza Sindi Mamba, Antony Ngunyi, Lawrence Nderu
KEYWORDS:
Optimized Gated Recurrent Unit, Approximation Coefficient, Stationary Wavelet Transform, Activation Function, Time Lag
JOURNAL NAME:
Journal of Data Analysis and Information Processing,
Vol.11 No.1,
February
8,
2023
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 Recurrent 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.