Visualizing And Forecasting Stocks Using Machine Learning

Abstract

Stock market forecasting is a critical area of
financial research due to its potential impact on
investment decisions and economic planning. This
study explores the application of machine learning
models, with a focus on Long Short-Term Memory
(LSTM) networks, for the purpose of forecasting
stock price trends and visualizing market patterns.
We employ time series analysis combined with
advanced data preprocessing techniques to train
our models on historical stock price data. The LSTM
model, well-suited for sequential data, is evaluated
for its ability to capture temporal dependencies and
long-term trends in stock behavior. Our results
indicate that the LSTM model significantly
outperforms traditional machine learning
algorithms in predictive accuracy. The model
achieved a high degree of accuracy and low root
mean square error (RMSE) in forecasting,
demonstrating its effectiveness in handling complex,
nonlinear patterns inherent in financial time series
data. Visualization tools were also integrated to
provide intuitive insights into predicted trends,
enabling better decision-making support. The
findings suggest that LSTM networks, when
combined with proper feature scaling and sequence
modeling, offer a robust approach to stock market
prediction.