This paper develops a machine learning framework for detecting and predicting liquidity sweep events in XAUUSD using event-based market microstructure analysis. Using 15-minute data from 2014–2024, the study formalizes liquidity sweeps as a binary classification problem evaluated through walk-forward validation. A calibrated Random Forest classifier demonstrates statistically meaningful predictive structure while also revealing a significant prediction–execution gap: predictive accuracy does not necessarily translate into profitable trading performance under naive execution. The work contributes to quantitative finance, market microstructure research, and machine learning-based financial forecasting.
Human activity recognition (HAR) using sensor data allows the automatic detection of human behavior and actions in everyday environments. The development of scalable and privacy-preserving HAR systems is supported by the nonintrusive collection of time-series data using wearable…
Human activity recognition (HAR) using sensor data allows the automatic detection of human behavior and actions in everyday environments. The development of scalable and privacy-preserving HAR systems is supported by the nonintrusive collection of time-series data using wearable…
These are the data and results reported in the paper "A probabilistic digital twin framework for corrosion-fatigue prognosis of floating offshore wind turbines".
These are the data and results reported in the paper "A probabilistic digital twin framework for corrosion-fatigue prognosis of floating offshore wind turbines".