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.
