Neural network models with different input: An application on stock market forecasting
Mathematical Modeling and Computing,
Journal Year:
2025,
Volume and Issue:
12(1), P. 176 - 186
Published: Jan. 1, 2025
It
is
no
doubt
challenging
to
forecast
the
stock
market
accurately
in
reality
due
ever-changing
market.
Ever
since
Artificial
Neural
Networks
(ANNs)
have
been
recognized
as
universal
approximators,
they
are
extensively
used
forecasting
albeit
not
having
a
systematic
approach
identifying
optimal
input.
The
appropriate
number
of
significant
lags
time
series
corresponds
input
forecasting.
Hence,
this
study
aims
compare
effect
several
approaches
determining
lag
for
ANNs
prior
forecasting,
based
on
autocorrelation
function,
partial
Box–Jenkins
model
and
forward
selection.
performances
were
compared
with
benchmark
models,
namely
naïve
terms
error
magnitudes
trend
change
error.
In
study,
all
found
outperform
models
such
that
neural
network
trained
selected
from
selection
1
31
(ANN4)
best
it
achieved
highest
accuracy
lowest
mean
absolute
percentage
Contrary
expectations,
performed
poorly
series.
different
inputs
viable
quantitative
yet
more
research
required
better
understand
other
measurements
improve
performance
Language: Английский
A hybrid Gaussian process-integrated deep learning model for retrofitted building energy optimization in smart city ecosystems
Applied Energy,
Journal Year:
2025,
Volume and Issue:
388, P. 125643 - 125643
Published: March 19, 2025
Language: Английский
An innovative machine learning approach for slope stability prediction by combining shap interpretability and stacking ensemble learning
Environmental Science and Pollution Research,
Journal Year:
2025,
Volume and Issue:
unknown
Published: May 7, 2025
Language: Английский
FastLSM-AutoML: Fast, reliable, and robust end-to-end AutoML tool for producing a landslide susceptibility map
Stochastic Environmental Research and Risk Assessment,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Dec. 11, 2024
Language: Английский
A study of data-driven fault diagnosis and early warning systems for power batteries in new energy vehicles
Proceedings of the 2021 5th International Conference on Electronic Information Technology and Computer Engineering,
Journal Year:
2024,
Volume and Issue:
unknown, P. 1260 - 1266
Published: Oct. 18, 2024
Language: Английский