  We present a new open-source framework for forecasting in Python. Our
framework forms part of sktime, a more general machine learning toolbox for
time series with scikit-learn compatible interfaces for different learning
tasks. Our new framework provides dedicated forecasting algorithms and tools to
build, tune and evaluate composite models. We use sktime to both replicate and
extend key results from the M4 forecasting study. In particular, we further
investigate the potential of simple off-the-shelf machine learning approaches
for univariate forecasting. Our main results are that simple hybrid approaches
can boost the performance of statistical models, and that simple pure
approaches can achieve competitive performance on the hourly data set,
outperforming the statistical algorithms and coming close to the M4 winner.
