ImputeVIS: An Interactive Evaluator to Benchmark Imputation Techniques for Time Series Data

Abstract

With the emergence of The Internet of Things (IoT), smart sensors have become abundant in our daily lives. Failures are very common in those devices, leaving the recorded time series with missing blocks of consecutive values. A cottage industry of imputation algorithms exists, each with different performance tradeoffs. The diversity in time series features, missingness patterns, and algorithms' categories makes it challenging to select the best algorithm. In this demonstration, we showcase ImputeVIS, an analytical tool for benchmarking imputation algorithms. ImputeVIS provides an optimal configuration of those algorithms by implementing various AutoML parameterization strategies. Moreover, it uncovers the behavior of imputation algorithms by explaining the interplay between time series features and the imputation results. Its interactive web browser interface allows users to simulate real-world sensor malfunctions by contaminating time series with different missing block scenarios, deploy their imputation algorithms, and compare them against various popular imputation families.