Adaptive Ensemble Imputation for Trip Data using Deep Reinforcement Learning (AEI-DRL)

Missing data imputation of transportation occupancy data faces a big challenge, specifically in situations where the entire trip of a vehicle is missing due to no sensors. Traditional imputation algorithms such as k-nearest neighbor (KNN), Multi-recurrent Neural Network (MRNN), Bidirectional recurrent imputation for time series (BRITS), and Transformer-based algorithms like self-attention-based imputation for time series (SAITS) and time-aware-self-attention-based imputation for time series (TA-SAITS) usually have poor adaptiveness to dataset variations since their performances depend highly on dataset characteristics. Even typical ensemble techniques such as bagging, boosting, and stacking do not update dynamically regarding various patterns in the same dataset. To overcome those challenges, this work proposes an Adaptive Ensemble Imputation based on Deep Reinforcement Learning, AEI-DRL. AEI-DRL uses a sliding window approach to scroll over sub-parts of the data and learns the optimal weights assignment of multiple imputation methods. This enables AEI-DRL to dynamically adjust weightage towards alternative algorithms or their combinations for different data segments. Experimental results show improvements in imputation results using AEI-DRL. It achieved a 5.3% and 2.6% reduction in mean square error (MSE) on the transportation occupancy dataset compared with the second-best method. On a non-transport dataset, AEI-DRL was able to record a reduction of MSE 16%, hence proving its applicability across time-series datasets with all sorts of diverse missing data patterns.