  U.S. Nonfarm employment is considered one of the key indicators for assessing
the state of the labor market. Considerable deviations from the expectations
can cause market moving impacts. In this paper, the total U.S. nonfarm payroll
employment is predicted before the release of the BLS employment report. The
content herein outlines the process for extracting predictive features from the
aggregated payroll data and training machine learning models to make accurate
predictions. Publically available revised employment report by BLS is used as a
benchmark. Trained models show excellent behaviour with R2 of 0.9985 and 99.99%
directional accuracy on out of sample periods from January 2012 to March 2020.
  Keywords Machine Learning; Economic Indicators; Ensembling; Regression, Total
Nonfarm Payroll
