Data Imputation Method based on Programming by Example: APREP-S

It has recently become possible to analyze integrated data obtained from sensors or wearable devices in the field of Information Technology (IT). Well-known examples include behavioral patterns of customers in a shop, the autonomous motion of robots, and fault prediction. It is known that the pre-processing of data - before it is input into the analysis model - is essential to achieve accurate results. However, this task requires 80% of the resources in the analysis process. As an improvement to the pre-processing of outliers and missing data, we had previously proposed “APREP-S” based on Bayesian inference with Programming by Example. In this paper, we expand APREP-S and define the timing of model generation for increased interactivity between analysts and APREP-S. We calculate the accuracy of the new APREP-S and compared it to that of existing methods. Based on our results, we conclude that APREP-S is the most effective imputation method for time-series data.