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Intern
    Chair of Computer Science II - Software Engineering

    Telescope

    Telescope

    Telescope is a hybrid forecasting tool written in R and designed to perform multi-step-ahead forecasts for univariate time series while maintaining a short runtime. The forecasting method is based on STL time series decomposition. To achieve better forecasting results, Telescope uses clustering techniques for categorical information creation and ARIMA, ANN, and XGBoost as forecasting methods. Telescope users can pass a matrix of timestamps and observation values, set the length of the forecasting horizon, and also set various optional parameters.

    Download:

    • Telescope is published under GPL v3 here
    • ITISE 2017 Presentation Slides available here
    • FAS Tutorial on "Best practices for Time Series Forecasting" Slides available here

    Installation:

    This package can be installed in R by using the following commands:

    install.packages("devtools")
    devtools::install_github("DescartesResearch/telescope")

    For unknown reasons, install_gitub does not work under all Windows versions. Therefore the package can alternatively be installed in R with the following commands:

    install.packages("remotes")
    remotes::install_url(url="https://github.com/DescartesResearch/telescope/archive/master.zip", INSTALL_opt= "--no-multiarch")

    Publications

    2020

    • Time Series Forecasting for Self-Aware Systems. A. Bauer; M. Züfle; N. Herbst; A. Zehe; A. Hotho; S. Kounev; in Proceedings of the IEEE (2020). 108(7) 1068–1093.
       
    • Telescope: An Automatic Feature Extraction and Transformation Approach for Time Series Forecasting on a Level-Playing Field. A. Bauer; M. Züfle; N. Herbst; S. Kounev; V. Curtef; in Proceedings of the 36th International Conference on Data Engineering (ICDE) (2020).
       

    2019

    • Challenges and Approaches: Forecasting for Autonomic Computing. A. Bauer; in Organic Computing: Doctoral Dissertation Colloquium 2018, S. Tomforde, B. Sick (Hrsg.) (2019).
       
    • Methoden und Messverfahren für Automatisches Skalieren in Elastischen Cloud Umgebungen. N. Herbst; (2019, Juni).
       
    • Best Practices for Time Series Forecasting. A. Bauer; M. Züfle; N. Herbst; S. Kounev; in 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W) (2019).
       

    2018

    • Comparing Machine Learning Approaches for Multivariate Time Series Forecasting. D. Otto; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2018, April).
       
    • Automatisiertes Forecasting mit Feature Engineering. B. Augustin; Thesis; University of Würzburg; Am Hubland, Informatikgebäude, 97074 Würzburg, Germany. (2018, April).
       
    • Predictive Maintenance for Industry 4.0. M. Züfle; (2018, April).
       
    • Methods and Benchmarks for Auto-Scaling Mechanisms in Elastic Cloud Environments. N. Herbst; (2018, Juli).
       
    • How we built a scalable micro-service application - The Tea Store. N. Herbst; (2018, Juli).
       

    2017

    • Telescope: A Hybrid Forecast Method for Univariate Time Series. M. Züfle; A. Bauer; N. Herbst; V. Curtef; S. Kounev; in Proceedings of the International work-conference on Time Series (ITISE 2017) (2017).
       

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