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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1007/s10618-023-00919-7
|2 doi
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|a eng
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1 |
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|a Farhangi, Ashkan
|e verfasserin
|4 aut
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|a AA-forecast
|b anomaly-aware forecast for extreme events
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|c 2023
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 12.04.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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|a Time series models often are impacted by extreme events and anomalies, both prevalent in real-world datasets. Such models require careful probabilistic forecasts, which is vital in risk management for extreme events such as hurricanes and pandemics. However, it's challenging to automatically detect and learn from extreme events and anomalies for large-scale datasets which often results in extra manual efforts. Here, we propose an anomaly-aware forecast framework that leverages the effects of anomalies to improve its prediction accuracy during the presence of extreme events. Our model has trained to extract anomalies automatically and incorporates them through an attention mechanism to increase the accuracy of forecasts during extreme events. Moreover, the framework employs a dynamic uncertainty optimization algorithm that reduces the uncertainty of forecasts in an online manner. The proposed framework demonstrated consistent superior accuracy with less uncertainty on three datasets with different varieties of anomalies over the current prediction models
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|a Journal Article
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| 650 |
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4 |
|a Anomaly decomposition
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| 650 |
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4 |
|a Time series forecasting
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| 650 |
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4 |
|a Uncertainty optimization
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| 700 |
1 |
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|a Bian, Jiang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Huang, Arthur
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xiong, Haoyi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Jun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Guo, Zhishan
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t Data mining and knowledge discovery
|d 2003
|g 37(2023), 3 vom: 30., Seite 1209-1229
|w (DE-627)NLM191691062
|x 1384-5810
|7 nnns
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| 773 |
1 |
8 |
|g volume:37
|g year:2023
|g number:3
|g day:30
|g pages:1209-1229
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|u http://dx.doi.org/10.1007/s10618-023-00919-7
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