Abstract: An Artificial Intelligence (AI)-based data remediation system accesses analyzes a dataset for identification and remediation of anomalies including missing values and outliers. Based on the attributes associated with the anomalies, the dataset is initially processed via statistical checks for identifying if the dataset includes one of point anomalies, contextual anomalies and time trend anomalies. Based on the type of anomalies various machine learning (ML) based techniques can be employed for identification of the anomalous data points. Expected values are generated. The anomalous data points are replaced with the expected values for the generation of a transformed dataset.

Abstract: An Artificial Intelligence (AI)-based data remediation system accesses analyzes a dataset for identification and remediation of anomalies including missing values and outliers. Based on the attributes associated with the anomalies, the dataset is initially processed via statistical checks for identifying if the dataset includes one of point anomalies, contextual anomalies and time trend anomalies. Based on the type of anomalies various machine learning (ML) based techniques can be employed for identification of the anomalous data points. Expected values are generated. The anomalous data points are replaced with the expected values for the generation of a transformed dataset.