A Time-Aware Machine Learning Framework for Behavioral Anomaly Monitoring and Short-Horizon Forecasting in Goats Using RFID-Derived Activity Data

Precision livestock farming requires monitoring approaches that extend beyond static activity thresholds to enable dynamic, animal-level decision support. A time-aware machine learning framework was developed to transform RFID-derived goat activity records into an interpretable behavioral monitoring system. Time-stamped activity data were processed into temporal features such as lagged activity, rolling mean, activity change, and elapsed-time variables to capture short-term behavioral history. The framework integrated latent-state discovery, fuzzy-uncertainty analysis, transition modeling, supervised classification, short-horizon forecasting, and dashboard-based alert visualization within a predictive dashboard-based monitoring framework. Four latent behavioral clusters were identified, with the dominant cluster representing a stable low-activity baseline and accounting for 77.88% of observations. Boundary-zone analysis indicated that 7.30% of observations were in transitional regions, while fuzzy clustering classified 21.42% as uncertain or mixed-state points, suggesting gradual shifts in activity. Transition analysis revealed greater persistence in baseline states and lower persistence in high-activity/non-baseline states, which exhibited the highest volatility and entropy. Using nested time-blocked validation, Random Forest predicted future high-activity/non-baseline onset with AUC values of 0.869 and 0.841 for 5 and 10 min horizons, respectively. These results demonstrate that activity instability can be detected and forecasted over short horizons, supporting behavior-based monitoring. However, external biological validation is still required before implementation as a health- or disease-detection system.