Impact of Data Versioning on Longitudinal Analytical Model Performance
DOI:
https://doi.org/10.56472/ICCSAIML25-155Keywords:
Data Versioning, Longitudinal Analysis, Model Performance Drift, Temporal Data, Reproducibility, Predictive Modeling, Data Management, Time-Series, Machine Learning, Data LineageAbstract
In data-driven environments where datasets evolve over time, the challenge of maintaining consistent and high-performing analytical models becomes increasingly critical. This paper investigates the impact of data versioning on the performance of longitudinal analytical models. We explore how changes in data over time, captured through systematic versioning, influence model accuracy, stability, and generalizability. Using real-world longitudinal datasets and a comparative modeling framework, we assess various data versioning strategies and their effects on predictive performance. Our findings reveal that integrating data versioning not only enhances reproducibility but also enables more robust handling of performance drift over time. This research offers practical insights for data scientists and engineers aiming to maintain the fidelity of analytical systems in dynamic environments
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