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

Telescope: An Automated Hybrid Forecasting Approach on a Level-Playing Field