A Comparison of ARIMA and XGBoost Models For Time Series Analysis Utilizing Human Behavioral Data

Abstract

Time series modeling is a powerful tool utilized across multiple domains to assess the underlying stochastic mechanisms in a dataset or to predict future values based on past values in the series. Time series forecasting has been used for many applications including the stock market, healthcare, and environmental sciences. Traditional models like ARIMA struggle with more sophisticated datasets that may have non-linear patterns, whereas more advanced machine learning models were created to handle those relationships. Despite the wide range of uses for time series modeling, use in psychology is limited. We propose by better understanding these models’ forecasting abilities with human behavioral datasets, time series can be used in various psychological and human factors applications such as monitoring and predicting behavior for improved interface design. Our work uses this tool to predict future values in a specified time trial in two human behavioral datasets. We compare the performance of ARIMA models and XGBoost models to evaluate the strengths and weaknesses of both models and establish which model performed best in our chosen evaluation metrics. Overall, ARIMA had more favorable values across performance metrics in most conditions, although XGBoost models still had well-performing scores. Although the models in our work performed well, the data needed to possess a stable mean and variance to utilize them. This requirement led to a loss of the trend throughout the time trial that was unique to each conditions’ effect on participants. Future research can utilize what we learned to work towards predictive time series models that accurately capture the unique trend of human behavioral data for more enhanced interface design

Keywords: Time series, ARIMA, XGBoost

DOI: 10.54941/ahfe1006052