Predicting Financial Enterprise Stocks and Economic Data Trends Using Machine Learning Time Series Analysis
Опубликована: Июль 11, 2024
This
paper
explores
the
application
of
machine
learning
in
financial
time
series
analysis,
focusing
on
predicting
trends
enterprise
stocks
and
economic
data.
It
begins
by
distinguishing
from
elucidates
risk
management
strategies
stock
market.
Traditional
statistical
methods
such
as
ARIMA
exponential
smoothing
are
discussed
terms
their
advantages
limitations
forecasting.
Subsequently,
effectiveness
techniques,
particularly
LSTM
CNN-BiLSTM
hybrid
models,
market
prediction
is
detailed,
highlighting
capability
to
capture
nonlinear
patterns
dynamic
markets.
The
study
demonstrates
advancements
predictive
accuracy
robustness
achieved
deep
through
empirical
analysis
model
validation.
findings
contribute
significantly
academic
discourse
offer
practical
insights
for
investors,
analysts,
policymakers
navigating
volatility
optimizing
investment
strategies.
Finally,
outlines
prospects
forecasting,
laying
a
theoretical
foundation
methodological
framework
achieving
more
precise
reliable
predictions.
Язык: Английский
Artificial Intelligence in Risk Protection for Financial Payment Systems
Опубликована: Июль 15, 2024
In
today's
highly
digitized
and
globalized
financial
environment,
the
need
to
protect
payment
systems
from
risk
is
more
urgent
than
ever.
Artificial
intelligence
(AI)
technology
rapidly
becoming
a
key
tool
in
this
space,
with
machine
learning
algorithms,
big
data
analytics,
real-time
monitoring
enabling
AI
effectively
detect
prevent
fraudulent
activity,
optimize
management
processes,
deliver
intelligent
services.
not
only
improves
security
efficiency
of
systems,
but
also
significantly
customer
satisfaction
loyalty.
With
continuous
development
technology,
industry
will
usher
innovations
changes
future,
further
optimizing
process,
improving
efficiency,
ensuring
security.
Язык: Английский