  In many areas of decision-making, forecasting is an essential pillar.
Consequently, many different forecasting methods have been proposed. From our
experience, recently presented forecasting methods are computationally
intensive, poorly automated, tailored to a particular data set, or they lack a
predictable time-to-result. To this end, we introduce Telescope, a novel
machine learning-based forecasting approach that automatically retrieves
relevant information from a given time series and splits it into parts,
handling each of them separately. In contrast to deep learning methods, our
approach doesn't require parameterization or the need to train and fit a
multitude of parameters. It operates with just one time series and provides
forecasts within seconds without any additional setup. Our experiments show
that Telescope outperforms recent methods by providing accurate and reliable
forecasts while making no assumptions about the analyzed time series.
