Exemplary listing of model performances calculated by the AML tool for ascariasis.

The AutoTS tool automatically creates and selects time series features in the modeling data and will automatically detect whether or not a project’s target value is stationary (that is, whether the statistical properties of the target are constant over time). If the target is not stationary, the AutoTS tool attempts to make it stationary by applying a differencing strategy prior to modeling. This improves the accuracy and robustness of the underlying models. This differencing strategy includes calculating difference of the time series itself with either the most recent value (latest) or the average baseline as seen in the column ’Feature List and Sample Size’. The optimization metric used was MAPE (mean absolute percentage error). The ’All Backtests Score’ represents the average of all backtests. The model types considered during the model selection process included the following 8 out of 24 models, which are sorted by the holdout score. The Performance Clustered eXtreme Gradient Boosted Trees Regressor model was further used for prediction since it rendered the best MAPE score.