Enhancing Animal Shelter Operations with Time Series and Machine Learning

Enhancing animal shelter operations through machine learning involves employing a variety of advanced techniques aimed at increasing efficiency, promoting animal welfare, and optimizing resource allocation. This paper explores predictive analytics for adoption rates using regression models to estimate the likelihood of adoption based on historical data, encompassing variables such as breed, health status, and previous adoption trends. Additionally, classification algorithms are utilized to categorize animals by adoption probability, facilitating better resources and marketing prioritization. Clustering algorithms are employed to group animals according to behavior patterns and/or physical health, enabling tailored medical care and enrichment activities that improve their mental and physical well-being. Time series (ARIMA) forecasting is applied to predict future animal intake capacity influencing inventory management and room allocation. Random forest and neural networks (LSTM) are integrated as supplemental predictive models, offering deep learning capabilities for more complex data patterns and serve as an additional tool in shelter decision-making processes. A key goal of this research is to reduce overcrowding/euthanasia rates, increase adoption rates, and increase Return to Owner (RTO) rates at the Dallas Animal Shelter by forecasting animal intake and predicting length of stay. The integration of these machine learning methodologies demonstrates enhancements in the operational effectiveness of animal shelters, ensuring that both animal care and resource utilization are maintained athigh standards. The findings highlight the potential of these techniques to enhance shelter management, promote more successful adoption outcomes, and reduce overcrowding/euthanasia rates.