Abstract:
Financial market, a very vital and large sector offinance, includes a large number of
investors, buyers and sellers. Financial market trends prediction has been a
discovered and introduced. But very few phenomenon since machine learning
approaches became useful for forecasting the financial market as it changes with the
passage of time. The objective of this project is to develop a software which can
predict and forecast the financial market trends and stocks with the help of machine
learning approach. Time Series approach has been used in our project in order to
trend ofthe items. Time series have many models and we have to fit the
model in order to forecast. Then, Seasonal & Trend Decomposition by
was
forecast the
appropriate
Loess Forecasting (STLF) is used as it has been widely applied in monetary and
for its extraordinary and great efficiency for financial market financial sectors
prediction.
The STLF Model have two main components which is Trend & Seasonality. Trend
means decrease in the quantity of data & Seasonality means any an increase or
specific season or time ofthe year. This model works when the combination oftrend
and seasonality occurs. We got some
y helpful for the investors in order to invest money on those products which are in
accurate results through this model which is
ver
higher demand.
