bib

papers/benchmarking/sample.bib

@@ -7,3 +7,126 @@ @article{greenwade93
     number  = "3",
     pages   = "342--351"
 }
+
+
+@inproceedings{Stankeviciute2021,
+ author = {Stankeviciute, Kamile and M. Alaa, Ahmed and van der Schaar, Mihaela},
+ booktitle = {Advances in Neural Information Processing Systems},
+ editor = {M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan},
+ pages = {6216--6228},
+ publisher = {Curran Associates, Inc.},
+ title = {Conformal Time-series Forecasting},
+ url = {https://proceedings.neurips.cc/paper_files/paper/2021/file/312f1ba2a72318edaaa995a67835fad5-Paper.pdf},
+ volume = {34},
+ year = {2021}
+}
+
+
+@book{hyndman2018,
+  title={Forecasting: principles and practice},
+  author={Hyndman, Rob J and Athanasopoulos, George},
+  year={2018},
+  publisher={OTexts}
+}
+
+@article{bergmeir2016,
+  title={Bagging exponential smoothing methods using STL decomposition and Box--Cox transformation},
+  author={Bergmeir, Christoph and Hyndman, Rob J and Ben{\'\i}tez, Jos{\'e} M},
+  journal={International journal of forecasting},
+  volume={32},
+  number={2},
+  pages={303--312},
+  year={2016},
+  publisher={Elsevier}
+}
+
+@article{phipps2024,
+  title={Generating probabilistic forecasts from arbitrary point forecasts using a conditional invertible neural network},
+  author={Phipps, Kaleb and Heidrich, Benedikt and Turowski, Marian and Wittig, Moritz and Mikut, Ralf and Hagenmeyer, Veit},
+  journal={Applied Intelligence},
+  pages={1--29},
+  year={2024},
+  publisher={Springer}
+}
+
+@article{wang2020,
+  title={Modeling load forecast uncertainty using generative adversarial networks},
+  author={Wang, Yi and Hug, Gabriela and Liu, Zijie and Zhang, Ning},
+  journal={Electric Power Systems Research},
+  volume={189},
+  pages={106732},
+  year={2020},
+  publisher={Elsevier}
+}
+
+@article{matheson1976,
+  title={Scoring rules for continuous probability distributions},
+  author={Matheson, James E and Winkler, Robert L},
+  journal={Management science},
+  volume={22},
+  number={10},
+  pages={1087--1096},
+  year={1976},
+  publisher={INFORMS}
+}
+
+@dataset{Godahewa2021Sunspot,
+  author       = {Godahewa, Rakshitha and
+                  Bergmeir, Christoph and
+                  Webb, Geoff and
+                  Hyndman, Rob and
+                  Montero-Manso, Pablo},
+  title        = {Sunspot Daily Dataset (without Missing Values)},
+  month        = mar,
+  year         = 2021,
+  publisher    = {Zenodo},
+  version      = 4,
+  doi          = {10.5281/zenodo.4654722},
+  url          = {https://doi.org/10.5281/zenodo.4654722}
+}
+
+@dataset{Godahewa2021USBirth,
+  author       = {Godahewa, Rakshitha and
+                  Bergmeir, Christoph and
+                  Webb, Geoff and
+                  Hyndman, Rob and
+                  Montero-Manso, Pablo},
+  title        = {US Births Dataset},
+  month        = apr,
+  year         = 2021,
+  publisher    = {Zenodo},
+  version      = 2,
+  doi          = {10.5281/zenodo.4656049},
+  url          = {https://doi.org/10.5281/zenodo.4656049}
+}
+
+@dataset{Godahewa2021Australian,
+  author       = {Godahewa, Rakshitha and
+                  Bergmeir, Christoph and
+                  Webb, Geoff and
+                  Hyndman, Rob and
+                  Montero-Manso, Pablo},
+  title        = {Australian Electricity Demand Dataset},
+  month        = apr,
+  year         = 2021,
+  publisher    = {Zenodo},
+  version      = 1,
+  doi          = {10.5281/zenodo.4659727},
+  url          = {https://doi.org/10.5281/zenodo.4659727}
+}
+
+@article{Assimakopoulos2000,
+title = {The theta model: a decomposition approach to forecasting},
+journal = {International Journal of Forecasting},
+volume = {16},
+number = {4},
+pages = {521-530},
+year = {2000},
+note = {The M3- Competition},
+issn = {0169-2070},
+doi = {https://doi.org/10.1016/S0169-2070(00)00066-2},
+url = {https://www.sciencedirect.com/science/article/pii/S0169207000000662},
+author = {V. Assimakopoulos and K. Nikolopoulos},
+keywords = {M3-Competition, Time series, Univariate forecasting method},
+abstract = {This paper presents a new univariate forecasting method. The method is based on the concept of modifying the local curvature of the time-series through a coefficient ‘Theta’ (the Greek letter θ), that is applied directly to the second differences of the data. The resulting series that are created maintain the mean and the slope of the original data but not their curvatures. These new time series are named Theta-lines. Their primary qualitative characteristic is the improvement of the approximation of the long-term behavior of the data or the augmentation of the short-term features, depending on the value of the Theta coefficient. The proposed method decomposes the original time series into two or more different Theta-lines. These are extrapolated separately and the subsequent forecasts are combined. The simple combination of two Theta-lines, the Theta=0 (straight line) and Theta=2 (double local curves) was adopted in order to produce forecasts for the 3003 series of the M3 competition. The method performed well, particularly for monthly series and for microeconomic data.}
+}