| dc.description.abstract | Abstract: The apparel industry is one of the
world’s major upcoming trends of industrial,
economical science. Apparel industry has
interconnected design producing and
manufacturing issues have become a greater
concern. In the domain of apparel product
manufacturing and marketing optimization
and prediction the design has played a
significant part of increasing productivity,
overall profit, the consumer demand, and
requirements towards the actual factory.
Industry has been challenged over and over
before adapted by adopting new methods of
designing and predict the optimum garment
based on the past records and analytical data
sets. In my study Time series Analysis and
the trained model is used to determine and
predict the optimal product under various
production constraints. Time Series Analysis
is one of the most accurate data analysis and
forecasting technique and it is widely used in
this research works. There are various kinds
of both the traditional statistical methods
and the more advanced artificial intelligence
(AI) techniques that have been used in
various existing systems in relevant to this
domain. Both those methods may suffer
considerable drawbacks in which the
former’s performance depend highly on the
time series data’s features whereas the latter
ones are slow. Hence there need to pay
attention for development of an intelligent
time series forecasting system which is fast,
versatile and can achieve a reasonably high
accuracy. Anyhow with the development of
computer technology, automated apparel
management systems and Machine Learning
models are latest popular, especially in
products classification and prediction. The
proposed work provides analytical
inferences from historical data of sales
records for apparel industry and modelling
them using time series analytics to make
effective decisions by predicting and
visualizing. | en_US |
