Adaptive Multi-agent System for Dynamic Missing Value Imputation in Time Series

This study introduces a novel adaptive multi-agent system (MAS) for dynamic missing value imputation in time series data, essential for accurate forecasting. The MAS employs a hybrid approach, intelligently selecting and combining techniques like regression, interpolation, and rule-based methods based on missing data patterns. Its extensible architecture allows seamless integration of new imputation methods. The system adapts dynamically, using regression for large gaps and statistical methods for smaller ones, with concurrent, restartable agents enhancing efficiency. A knowledge-capture mechanism enables continuous learning by storing imputed data and context for future use. The MAS was extensively evaluated on a real-world human mobility dataset with over 100 million records from Hiroshima, Japan, serving as a preprocessing step in an AutoML platform for time series forecasting. Tests on vehicle movement data and Airline passenger data demonstrated its effectiveness, with low evaluation metrics confirming its accuracy and efficiency. The proposed MAS offers a robust, scalable solution for missing value imputation in diverse time series applications.