Hybrid Intelligence For Financial Forecasting Uniting Lstm With Xg Boost

Abstract

Predicting Price Of Any Stock Is Difficult To Achieve Because Of Volatility Of Market Conditions. In Fraction Of Seconds Markets Go Up And Down, Fluctuations And Mood Of The Investor These Makes Traditional Methods Less Accurate To Predict. Normal Statistical Methods Do Not Detect Small Changes In Stock Market Even Though That Are Important For Understanding The Market And Many Of The Times Single Ml Model Become Unstable With Market Changes. In This Study, We Tried A Prediction Method That Combines The ‘Long Term Short Memory’ Network With The Gradient Boosting Procedure (XG Boost). Long Term Short Memory (LSTM) Helps To Understand How The Price Changes With The Time And It Finds The Underlying Timely Based Pattern In Past Data(historical) Related To Stocks. These Observations, Combined With A Group Of Produced Market Measures Are Given Or Passes To The XG Boost, Which Helps Show Patterns And Irregular Movements That The LSTM Model Alone Skips Or Failed To Notice. The Objective Of This Mixed Approach Is To Form Predictions That Remain Accurate In Various Or Changing Conditions Of The Market And Give More Accurate Changing Pattern(nature) Of Stock Data. Tests Conducted On NIFTY 50 Index And Various NSE- Listed Stocks Reveal That The Integrated Approach Outperforms Isolated LSTM, XG Boost, And Baseline Models, As Measured By Lower RMSE And MAE Values, Underscoring Its Reliability And Real-world Potential.

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