STOCK MARKET PREDICTION PORTAL USING xLSTM-TS MODEL

Stock market volatility is one of the biggest challenges in the financial sector, and accurate predictions are hard to make but important for proper investment strategies. Recent research in deep learning has shown some promising improvements in prediction accuracy. This paper introduces the development of the Stock Vision Tool, a deep learning model for stock price forecasting, using the xLSTM-TS model further using wavelet denoising for preprocessing and cleansing financial data for optimizing training outcomes. Google Trends data was also incorporated for market sentiment analysis. This further strengthened the forecasting capabilities and enhanced the prediction capabilities to reveal deeper aspects of market behavior and more accurate forecasts overall.