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
phenomenon since machine learning was discovered and introduced. But very few
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
forecast the trend ofthe items. Time series have many models and we have to fit the
appropriate model in order to forecast. Then, Seasonal & Trend Decomposition by
Loess Forecasting (STLF) is used as it has been widely applied in monetary and
financial sectors for its extraordinary and great efficiency for financial market
prediction.
The STLF Model have two main components which is Trend & Seasonality. Trend
means an increase or decrease in the quantity of data & Seasonality means any
specific season or time ofthe year. This model works when the combination oftrend
and seasonality occurs. We got some accurate results through this model which is
very helpful for the investors in order to invest money on those products which are in
higher demand.
