Systematic review highlights improved missing data imputation in spatio-temporal contexts, suggesting advancements in statistical and machine learning methods.
Key Points
This work aims to review techniques for spatio-temporal missing data imputation, focusing on statistical and machine learning approaches.
Conducted a systematic literature review
Analyzed traditional statistical and modern machine learning methods
Identified challenges and research gaps in the imputation process
Highlighted advancements in methods leveraging both spatial and temporal dependencies
Reported improvements in data reconstruction accuracy
Identified the majority of research concentrated on statistical and machine learning techniques