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Large-Scale Time Series Analytics

Novel Approaches for Generation and Prediction

Abstract

More and more data is gathered every day and time series are a major part of it. Due to the usefulness of this type of data, it is analyzed in many application domains. While there already exists a broad variety of methods for this task, there is still a lack of approaches that address new requirements brought up by large-scale time series data like cross-domain usage or compensation of missing data. In this paper, we address these issues, by presenting novel approaches for generating and forecasting large-scale time series data.

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Funding

This work was funded by the German Federal Ministry of Education and Research within the project Competence Center for Scalable Data Services and Solutions Phase 1—ScaDS Dresden/Leipzig (BMBF 01IS14014A).

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Correspondence to Claudio Hartmann.

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Hahmann, M., Hartmann, C., Kegel, L. et al. Large-Scale Time Series Analytics. Datenbank Spektrum 19, 17–29 (2019). https://doi.org/10.1007/s13222-018-00304-5

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  • DOI: https://doi.org/10.1007/s13222-018-00304-5

Keywords

  • Data analytics
  • Time series generation
  • Time series forecasting
  • Big Data