Telescope: An Automated Hybrid Forecasting Approach on a Level-Playing Field
This work addresses the need for efficient and automated forecasting tools for decision-makers, though it appears incremental as it builds on existing machine learning concepts without a major paradigm shift.
The paper tackles the problem of computationally intensive, poorly automated, and dataset-specific forecasting methods by introducing Telescope, a machine learning-based approach that automatically processes time series and provides forecasts within seconds, outperforming recent methods in accuracy and reliability.
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.