Auflistung nach Autor:in "Kegel, Lars"
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- ZeitschriftenartikelFeature-aware forecasting of large-scale time series data sets(it - Information Technology: Vol. 62, No. 3-4, 2020) Hartmann, Claudio; Kegel, Lars; Lehner, WolfgangThe 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.
- JournalLarge-Scale Time Series Analytics(Datenbank-Spektrum: Vol. 19, No. 1, 2019) Hahmann, Martin; Hartmann, Claudio; Kegel, Lars; Lehner, Wolfgang
- ZeitschriftenartikelSeason- and Trend-aware Symbolic Approximation for Accurate and Efficient Time Series Matching(Datenbank-Spektrum: Vol. 21, No. 3, 2021) Kegel, Lars; Hartmann, Claudio; Thiele, Maik; Lehner, WolfgangProcessing and analyzing time series datasets have become a central issue in many domains requiring data management systems to support time series as a native data type. A core access primitive of time series is matching, which requires efficient algorithms on-top of appropriate representations like the symbolic aggregate approximation (SAX) representing the current state of the art. This technique reduces a time series to a low-dimensional space by segmenting it and discretizing each segment into a small symbolic alphabet. Unfortunately, SAX ignores the deterministic behavior of time series such as cyclical repeating patterns or a trend component affecting all segments, which may lead to a sub-optimal representation accuracy. We therefore introduce a novel season- and a trend-aware symbolic approximation and demonstrate an improved representation accuracy without increasing the memory footprint. Most importantly, our techniques also enable a more efficient time series matching by providing a match up to three orders of magnitude faster than SAX.