Stockinsight: LSTM-Based Stock Market Forecasting and Visual Analytics System

The paper present StockInsight, an end-to-end platform for interactive stock trend analysis and forecasting using Long Short-Term Memory (LSTM) deep learning. StockInsight integrates historical price data retrieval, preprocessing, model training, and web-based visualization. Historical stock data (Open, High, Low, Close, Volume) are obtained via the Yahoo Finance API for multiple equities over 10–15 years. Data cleaning and feature engineering (e.g., moving averages, sequence windows) are performed in Python using Pandas/Numpy. The forecasting core is a multivariate LSTM recurrent neural network with two LSTM layers followed by dense output layers, trained on 100-day rolling windows to predict next-day closing prices. evaluate the model using standard regression metrics (RMSE, MAE) and find it achieves substantial accuracy on test stocks. The web application (Flask-based) provides interactive charts of actual v/s predicted prices, forecast tables for the next 10 days, and trend visualizations. In experiments with tech-sector stocks, StockInsight’s LSTM forecasts closely track real price movements and improve on baseline ARIMA-like performance. Sample outputs include time-series plots of predictions v/s actual prices and tabulated multi-day forecasts. The system’s design, predictions, and user interface are discussed in detail, along with evaluation across varying market conditions, highlighting implications for retail traders.