Adjust past sales of each product to correct it for anomalies such as stock-outs, price variations, ad spend and other outliers. Main objective is to feed the forecasting models with sanitized data, in order to increase the accuracy of the forecasts. Flieber will automatically pre-process some of the data and will surface other data for user to manually identify/tag. User will have the ability to make corrections to the pre-processed data in case the algorithms make wrong assumptions.