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Licensed Unlicensed Requires Authentication Published by De Gruyter Oldenbourg March 6, 2020

Feature-aware forecasting of large-scale time series data sets

Claudio Hartmann ORCID logo, Lars Kegel and Wolfgang Lehner

Abstract

The Internet of Things (IoT) sparks a revolution in time series forecasting. Traditional techniques forecast time series individually, which becomes unfeasible when the focus changes to thousands of time series exhibiting anomalies like noise and missing values. This work presents CSAR, a technique forecasting a set of time series with only one model, and a feature-aware partitioning applying CSAR on subsets of similar time series. These techniques provide accurate forecasts a hundred times faster than traditional techniques, preparing forecasting for the arising challenges of the IoT era.

ACM CCS:

Funding source: European Regional Development Fund

Award Identifier / Grant number: 100320127

Funding source: Horizon 2020 Framework Programme

Award Identifier / Grant number: 731232

Funding statement: This work is partly funded (1) by the European Regional Development Fund (ERDF) under co-financing by the Free State of Saxony (100320127) and Systema GmbH, and (2) within the European Union’s Horizon 2020 research and innovation program under grant agreement No. 731232.

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Received: 2019-10-02
Revised: 2020-02-07
Accepted: 2020-02-21
Published Online: 2020-03-06
Published in Print: 2020-05-27

© 2020 Walter de Gruyter GmbH, Berlin/Boston