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

TL;DR

Telescope is a fast, automated machine learning-based forecasting method that efficiently handles individual time series without extensive parameter tuning, outperforming recent approaches in accuracy and reliability.

Contribution

Introduces Telescope, a novel, automated, and parameter-free forecasting approach that processes single time series quickly and effectively without requiring training or assumptions.

Findings

Outperforms recent forecasting methods in accuracy

Provides forecasts within seconds without setup

Operates effectively on individual time series

Abstract

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.