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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.<\/jats:p>","DOI":"10.1515\/itit-2019-0035","type":"journal-article","created":{"date-parts":[[2020,3,6]],"date-time":"2020-03-06T09:02:06Z","timestamp":1583485326000},"page":"157-168","source":"Crossref","is-referenced-by-count":0,"title":["Feature-aware forecasting of large-scale time series data sets"],"prefix":"10.1515","volume":"62","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5334-059X","authenticated-orcid":false,"given":"Claudio","family":"Hartmann","sequence":"first","affiliation":[{"name":"Technische Universit\u00e4t Dresden , Database Systems Group , Dresden , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lars","family":"Kegel","sequence":"additional","affiliation":[{"name":"Technische Universit\u00e4t Dresden , Database Systems Group , Dresden , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wolfgang","family":"Lehner","sequence":"additional","affiliation":[{"name":"Technische Universit\u00e4t Dresden , Database Systems Group , Dresden , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2020,3,6]]},"reference":[{"key":"2023033120254861550_j_itit-2019-0035_ref_001_w2aab3b7d332b1b6b1ab2ab1Aa","doi-asserted-by":"crossref","unstructured":"J\u2009G De Gooijer and R\u2009J Hyndman. 25 Years of Time Series Forecasting. 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