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The efficiency of business processes relies on business key performance indicators (Biz-KPIs), that can be negatively impacted by IT failures.
N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
Oreshkin, B. N.; Carpov, D.; Chapados, N.; and Bengio, Y. 2019 · 1905
Earlier work this paper cites.
Process mining: a research agenda
van der Aalst, W. M. P.; and Weijters, A. J. M. M. 2004 · 2004
Earlier work this paper cites.
New introduction to multiple time series analysis
Lütkepohl, H. 2005 · 2005
Earlier work this paper cites.
On the properties of neural machine translation: Encoder-decoder approaches
Cho, K.; Van Merriënboer, B.; Bahdanau, D.; and Bengio, Y. 2014 · 2014
Earlier work this paper cites.
Xgboost: A scalable tree boosting system
Chen, T.; and Guestrin, C. 2016 · 2016
Earlier work this paper cites.
Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
Earlier work this paper cites.
Adaptive graph convolutional recurrent network for traffic forecasting
Bai, L.; Yao, L.; Li, C.; Wang, X.; and Wang, C. 2020 · 2020
Earlier work this paper cites.
DeepAR: Probabilistic forecasting with autoregressive recurrent networks
Salinas, D.; Flunkert, V.; Gasthaus, J.; and Januschowski, T. 2020 · 2020
Earlier work this paper cites.
On the opportunities and risks of foundation models
Bommasani, R.; Hudson, D. A.; Adeli, E.; Altman, R.; Arora, S.; von Arx, S.; Bernstein, M. S.; Bohg, J.; Bosselut, A.; Brunskill, E.; et al. 2021 · 2021
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Temporal fusion transformers for interpretable multi-horizon time series forecasting
Lim, B.; Arık, S. Ö.; Loeff, N.; and Pfister, T. 2021 · 2021
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Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting
Wu, H.; Xu, J.; Wang, J.; and Long, M. 2021 · 2021
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Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Zhou, H.; Zhang, S.; Peng, J.; Zhang, S.; Li, J.; Xiong, H.; and Zhang, W. 2021 · 2021
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Probabilistic machine learning: an introduction
Murphy, K. P. 2022 · 2022
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Environmental Intelligence Suite
EIS. 2023 · 2023
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Stan’s Robot Shop
Instana. 2023 · 2023
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Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting
Jati, A.; Ekambaram, V.; Pal, S.; Quanz, B.; Gifford, W. M.; Harsha, P.; Siegel, S.; Mukherjee, S.; and Narayanaswami, C. 2023 · 2023
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PyTorch Forecasting
PyTorch. 2023 · 2023
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TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting
Vijay, E.; Jati, A.; Nguyen, N.; Sinthong, G.; and Kalagnanam, J. 2023 · 2023
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Are transformers effective for time series forecasting?
Zeng, A.; Chen, M.; Zhang, L.; and Xu, Q. 2023 · 2023
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A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
Nie, Y.; Nguyen, N. H.; Sinthong, P.; and Kalagnanam, J. 2022 · 2022
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FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting
Zhou, T.; Ma, Z.; Wen, Q.; Wang, X.; Sun, L.; and Jin, R. 2022 · 2022
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Lead-to-Cash process
SAP. 2023a
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Sales Order process
SAP. 2023b
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Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting
Zhang, Y.; and Yan, J. 2023 · 2023
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