A Comparative Study of Imputation Methods for Time Series Data


  • Daniyal Khan Youngstown State University
  • Alina Lazar Youngstown State University




Time series, Representation Learning, Graph, GRIN, CSDI, SAITS, PyPOTS, Model, Diffusion, SSSD, Tabular Data, partially-observed data, imputation


Missing and incomplete values pose a significant challenge in analyzing tabular and time-series data. Dealing with missing values is time-consuming and tedious, especially when working with data from real-world applications. While some imputation approaches estimate missing values based on existing observations, these methods often rely on strong assumptions about the data distribution, which only sometimes improves downstream accuracy. Although tabular imputation methods can be applied to time-series data, incorporating the time component can enhance accuracy. This study evaluates various techniques for missing data imputation in time-series data. We run experiments on four multi-variate time series datasets using five imputation methods. We report training time and testing accuracy.




How to Cite

Khan, D., & Lazar, A. . (2023). A Comparative Study of Imputation Methods for Time Series Data. The International FLAIRS Conference Proceedings, 36(1). https://doi.org/10.32473/flairs.36.133068