Telescope: An Automatic Feature Extraction and Transformation Approach for Time Series (ICDE 2020)
Channel: Descartes Research
Date: 17/04/2020

One central problem of machine learning is the inherent limitation to predict only what has been learned — stationarity. Any time series property that  eludes stationarity poses a challenge for the proper model building. Furthermore, existing forecasting methods lack reliable forecast accuracy and time-to-result if not applied in their sweet spot. In this paper, we propose a fully automated machine learning-based forecasting approach. Our Telescope approach extracts and transforms features from an input time series and uses them to generate an optimized forecast model. In a broad competition including the latest hybrid forecasters, established statistical, and machine learning-based methods, our Telescope approach shows the best forecast accuracy coupled with a lower and reliable time-to-result.