Feature-aware forecasting of large-scale time series data sets
| dc.contributor.author | Hartmann, Claudio | |
| dc.contributor.author | Kegel, Lars | |
| dc.contributor.author | Lehner, Wolfgang | |
| dc.date.accessioned | 2021-06-21T09:38:45Z | |
| dc.date.available | 2021-06-21T09:38:45Z | |
| dc.date.issued | 2020 | |
| dc.description.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. | en |
| dc.identifier.doi | 10.1515/itit-2019-0035 | |
| dc.identifier.pissn | 2196-7032 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/36568 | |
| dc.language.iso | en | |
| dc.publisher | De Gruyter | |
| dc.relation.ispartof | it - Information Technology: Vol. 62, No. 3-4 | |
| dc.subject | Data Analytics | |
| dc.subject | Time Series Forecasting | |
| dc.subject | Big Data | |
| dc.subject | IoT | |
| dc.title | Feature-aware forecasting of large-scale time series data sets | en |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 168 | |
| gi.citation.publisherPlace | Berlin | |
| gi.citation.startPage | 157 |