  Machine learning models benefit when allowed to learn from temporal trends in
time-stamped administrative data. These trends can be represented by dividing a
model's observation window into time segments or bins. Model training time and
performance can be improved by representing each feature with a different time
resolution. However, this causes the time bin size hyperparameter search space
to grow exponentially with the number of features. The contribution of this
paper is to propose a computationally efficient time series analysis to
investigate binning (TAIB) technique that determines which subset of data
features benefit the most from time bin size hyperparameter tuning. This
technique is demonstrated using hospital and housing/homelessness
administrative data sets. The results show that TAIB leads to models that are
not only more efficient to train but can perform better than models that
default to representing all features with the same time bin size.
