Stock price data is diverse and situational, and it is unlikely that any single model will be uniformly best across all industries or contexts. Based on our results, ARIMA GARCH methods are better for Consumer Discretionary and Financial industries, and LSTM models are better for Healthcare and Industrials. Specifically, we found Cumulative Year…
A comparative audit of 15+ time series architectures (ARIMA-GARCH, XGBoost, Transformers) across four temporal domains. Focuses on predictive robustness, residual adequacy (Ljung-Box, ARCH-LM), and statistical significance testing.
Predicting Natural Rubber Prices: An Analysis of ARIMA-GARCH, Exponential Smoothing and LSTM Approaches
To to design and backtest an active trading strategy for the S&P 500 that exploits short-term serial dependence in returns and time-varying volatility
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