Paper Title
Forecasting Stock Market Trends: Time Series Data Analytics Enhanced By Google Trends

Abstract
Stock market movement prediction is one of the most difficult tasks due to the innate volatility and complexity of financial markets. Traditional time series methods like ARIMA are good at capturing linear patterns but fail to capture the dynamic nonlinear nature that usually exists in markets. Integrate ARIMA with machine learning techniques like LSTM networks, which let us take advantage of both. While the ARIMA model works well in capturing linear components of the series, an LSTM network captures more complex nonlinear features with greater perfection. The proposed hybrid model offers not only improved accuracy in prediction but also more meaningful insights into market psychology, giving profound analysis of market trends. Further refinement is facilitated by the detection of early detection of market shifts and sectorspecific insights to develop proper investment strategies. This would surely be a powerful way for individual investors to make buy or sell decisions, and financial planners to mold recommendations for their clients to make much better decisions in investment management and financial advisory services. Keywords - Stock Market Forecasting, Market Volatility, Financial Market, Time Series Analysis, LSTM Networks, Predictive Modeling, Market Psychology, Machine Learning In Finance