Tagged data-science

A comparative study of neural network models (LSTM, CNN-LSTM) for S&P 500 index prediction. The analysis evaluates multiple architectures against baseline strategies like buy-and-hold and naive forecasting, finding that while specialized models like LSTM with Batch Normalization and Regularization improve technical error metrics (MSE, MAE). However, when tested with a simulated trading, beating market returns remains a significant challenge requiring further architectural and strategy refinement.

A comprehensive comparative study of time series forecasting methods across six diverse datasets (Gold, S&P 500, FTSE, USDC/USDT, StackOverflow questions, and cinema tickets). This analysis evaluates 10+ forecasting techniques—from naive baselines to ARIMA/SARIMA models—with quantitative performance metrics (MSE, RMSE, MAE), explores stationarity testing, seasonality decomposition, Monte Carlo simulation, and implements a simulated trading strategy.

An in depth exploration of stock market data through feature engineering and pattern recognition. This study analyzes historical price changes and volatility to identify trends across diverse companies using custom statistical metrics and datetime features.