Die chronologische Liste zeigt aktuelle Veröffentlichungen aus dem Forschungsbetrieb der Hochschule Weihenstephan-Triesdorf. Zuständig ist das Zentrum für Forschung und Wissenstransfer (ZFW).
8 Ergebnisse
Prof. Dr. Norbert Huber
Mobilität - Grün und Sauber (2021) 14. Wissenschaftstag der Europäischen Metropolregion Nürnberg am 30. September 2021 in Ansbach .
Cristina G. Mitincu,
Cristian Ioja,
Constantina Alina Hossu,
Prof. Dr. Martina Artmann,
Andreea Nita,
Dr. Mihai Nita
Berechtigungen: Peer Reviewed
Licensing sustainability related aspects in Strategic Environmental Assessment. Evidence from Romania’s
urban areas (2021) Land Use Policy 108 .
DOI: 10.1016/j.landusepol.2021.105572
Julia Helfert,
C. Felsheim,
Jürgen Niederstraßer,
Dr. Ute Appeltauer-Brandl,
Prof. Dr. Iryna Smetanska
Prof. Dr. Florian Haselbeck,
Prof. Dr. Dominik Grimm
EVARS-GPR: EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data (2021) 44th German Conference on Artificial Intelligence (Virtual Conference) .
DOI: 10.1007/978-3-030-87626-5_11
Time series forecasting is a growing domain with diverse applications. However, changes of the system behavior over time due to internal or external influences are challenging. Therefore, predictions of a previously learned forecasting model might not be useful anymore. In this paper, we present EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data (EVARS-GPR), a novel online algorithm that is able to handle sudden shifts in the target variable scale of seasonal data. For this purpose, EVARS-GPR combines online change point detection with a refitting of the prediction model using data augmentation for samples prior to a change point. Our experiments on simulated data show that EVARS-GPR is applicable for a wide range of output scale changes. EVARS-GPR has on average a 20.8% lower RMSE on different real-world datasets compared to methods with a similar computational resource consumption. Furthermore, we show that our algorithm leads to a six-fold reduction of the averaged runtime in relation to all comparison partners with a periodical refitting strategy. In summary, we present a computationally efficient online forecasting algorithm for seasonal time series with changes of the target variable scale and demonstrate its functionality on simulated as well as real-world data. All code is publicly available on GitHub: https://github.com/grimmlab/evars-gpr.
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