Time-series analysis for forecasting monthly workload at two elephant hospitals in Thailand

Abstract

The increasing demand for elephant healthcare in Thailand underscores the need for efficient resource management in veterinary hospitals. Treating sick elephants requires substantial medical inputs, and without accurate workload forecasting, hospitals risk shortages in staff, equipment, and medications, potentially compromising care quality. This study evaluated six time-series forecasting models— Autoregressive Integrated Moving Average and its seasonal model (ARIMA/SARIMA), Exponential Smoothing (ETS), Trigonometric, Box-Cox ARMA Trend Seasonality (TBATS), Neural Network Time Series Regression (NNTR), Prophet, and Extreme Gradient Boosting (XGBoost)—to predict monthly workloads at two major elephant hospitals: the National Elephant Institute (NEI) hospital in northern Thailand and the Department of Livestock Development (DLD) hospital in the northeast. Historical admission data spanning five years (NEI) and nine years (DLD) were analyzed. The ETS model achieved the highest accuracy at NEI, outperforming all other models by effectively capturing its stable and seasonal caseload patterns. In contrast, the NNTR model performed best at DLD, where it accommodated irregular fluctuations likely driven by external factors. Forecasts for 2025 suggested a consistent caseload at NEI (28–31 cases/month) and a declining trend at DLD, with the lowest projection in September. Although patient volume was low, each case demanded disproportionately high resources, justifying the need for anticipatory planning. This study provides the first demonstration that multi-model time-series forecasting can generate actionable insights using sparse, real-world data from wildlife hospitals. By embedding predictive analytics into routine operations, it offers a novel, scalable framework to support data-driven decision-making in endangered species care.