A MACHINE LEARNING APPROACH IN FINANCIAL MARKETS

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.