The
problem
of
demand
forecasting
for
e-commerce
retail
merchants
is
a
common
challenge
in
the
industry.
key
lies
how
to
accurately
predict
customer
and
solve
practical
problems
based
on
prediction
results.
research
this
topic
facilitates
platforms
adjust
their
inventory
timely
manner
so
as
not
only
satisfy
but
also
effectively
reduce
costs
operating
costs.
It
line
with
development
trend
industry,
such
intelligence,
personalization
customization,
integration
online
offline.
In
paper,
hybrid
ARIMA-LR
model
first
used
forecast
different
storage
sites
e-commerce.
This
combines
an
autoregressive
sliding
average
(ARIMA)
linear
regression
(LR)
improve
accuracy
stability.
Then,
genetic
algorithm
select
best
classification
metrics.
performance
metrics,
most
suitable
metrics
from
them
classify
time
series.
By
classifying
series
into
categories,
patterns
features
same
type
can
be
better
understood
analyzed.
helps
extract
useful
information,
identify
potential
trends
patterns,
provide
decision
support
management
warehouse
sites.
International Journal of Sustainable Development & World Ecology,
Год журнала:
2024,
Номер
31(5), С. 523 - 536
Опубликована: Янв. 7, 2024
China,
the
world's
largest
CO2
emitter,
has
pledged
to
reduce
its
carbon
intensity
by
18%
2025,
which
requires
accurate
forecasting
of
emissions
and
their
drivers.
However,
existing
gray
models
have
limitations
in
dealing
with
fluctuating
data
or
long-time
series
data,
they
often
suffer
from
overfitting
poor
generalization
ability.
Moreover,
there
is
a
lack
research
judgment
on
future
changes
emission
To
address
these
issues,
this
study
proposes
fractional
order
adaptive
rolling
model
(RFAGM(1,1))
that
optimizes
background
generation
incorporates
mechanism.
We
apply
RFAGM(1,1)
forecast
China's
emissions,
GDP,
population,
consumption
raw
coal,
crude
oil,
natural
gas
2020
2025.
Our
results
show
achieves
significantly
higher
accuracy
than
standard
models,
except
for
population.
The
projections
indicate
China
will
meet
reduction
target
Furthermore,
LMDI
decomposition
reveals
economic
growth
population
positive
cumulative
impacts
(245.68%
11.95%,
respectively),
while
energy
structural
negative
(−151.60%
−6.02%,
respectively).
improved
enables
evaluation
climate
policies,
factor
analysis
provides
valuable
insights
developing
evidence-based
strategies
achieve
peaking
neutrality
goals
2030/2060.
