Ensemble XGBoost and LSTM‐Based Predictive Analytics for Real‐Time Industry 4.0 Decision Support Systems

This chapter introduces an ensemble model that is based on integrating both XGBoost and LSTM models to provide powerful, low-latency predictive analytics in real-time Industry 4.0 decision support systems. It focuses on covering the design, implementation and determination of an ensemble predictive analytics system which could synergize XGBoost and LSTM models to enable decision-making models in real time within Industry 4.0 environments. It aims to cover overall single-model baselines; inference latency: operated by real-time constraints; stream robustness and non-stationary behavior; and interpretable outputs or post hoc explanations: the objective being to make human-in-the-loop decisions and create trust A combination model derived from gradient-boosted trees (XGBoost) and sequence models (LSTM) uses two separate advantages: XGBoost is capable of capturing sophisticated, nonlinear relationships in engineered features and performs quickly and interpretably, and LSTMs help capture long periods of temporal variation present in sensor streams.