Feature-aware forecasting of large-scale time series data sets
| Autor: | Lars Kegel, Wolfgang Lehner, Claudio Hartmann |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
General Computer Science
Scale (ratio) business.industry Computer science Information technology 02 engineering and technology computer.software_genre Feature (computer vision) 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Time series business computer |
| Zdroj: | it-Information Technology |
| ISSN: | 2196-7032 |
| DOI: | 10.1515/itit-2019-0035 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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