Citations

report/include/airtraffic.bib

@@ -53,6 +53,32 @@
   file = {/home/lars/Zotero/storage/WKKE4DRU/an-intuitive-explanation-of-lstm-a035eb6ab42c.html}
 }
 
+@article{harrisArrayProgrammingNumPy2020,
+  title = {Array Programming with {{NumPy}}},
+  author = {Harris, Charles R. and Millman, K. Jarrod and van der Walt, St{\'e}fan J. and Gommers, Ralf and Virtanen, Pauli and Cournapeau, David and Wieser, Eric and Taylor, Julian and Berg, Sebastian and Smith, Nathaniel J. and Kern, Robert and Picus, Matti and Hoyer, Stephan and van Kerkwijk, Marten H. and Brett, Matthew and Haldane, Allan and del R{\'i}o, Jaime Fern{\'a}ndez and Wiebe, Mark and Peterson, Pearu and {G{\'e}rard-Marchant}, Pierre and Sheppard, Kevin and Reddy, Tyler and Weckesser, Warren and Abbasi, Hameer and Gohlke, Christoph and Oliphant, Travis E.},
+  year = 2020,
+  month = sep,
+  journal = {Nature},
+  volume = {585},
+  number = {7825},
+  pages = {357--362},
+  publisher = {{Springer Science and Business Media LLC}},
+  doi = {10.1038/s41586-020-2649-2}
+}
+
+@article{hunterMatplotlib2DGraphics2007,
+  title = {Matplotlib: {{A 2D}} Graphics Environment},
+  author = {Hunter, J. D.},
+  year = 2007,
+  journal = {Computing in Science \& Engineering},
+  volume = {9},
+  number = {3},
+  pages = {90--95},
+  publisher = {IEEE COMPUTER SOC},
+  doi = {10.1109/MCSE.2007.55},
+  abstract = {Matplotlib is a 2D graphics package used for Python for application development, interactive scripting, and publication-quality image generation across user interfaces and operating systems.}
+}
+
 @misc{nntikz,
   title = {{{NNTikZ}}: {{TikZ}} Diagrams for Deep Learning and Neural Networks},
   author = {Love, Fraser},
@@ -60,6 +86,24 @@
   publisher = {GitHub}
 }
 
+@misc{paszkePyTorchImperativeStyle2019,
+  title = {{{PyTorch}}: {{An Imperative Style}}, {{High-Performance Deep Learning Library}}},
+  shorttitle = {{{PyTorch}}},
+  author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and K{\"o}pf, Andreas and Yang, Edward and DeVito, Zach and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
+  year = 2019,
+  month = dec,
+  number = {arXiv:1912.01703},
+  eprint = {1912.01703},
+  primaryclass = {cs},
+  publisher = {arXiv},
+  doi = {10.48550/arXiv.1912.01703},
+  urldate = {2025-12-18},
+  abstract = {Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it provides an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several common benchmarks.},
+  archiveprefix = {arXiv},
+  keywords = {Computer Science - Machine Learning,Computer Science - Mathematical Software,Statistics - Machine Learning},
+  file = {/home/lars/Zotero/storage/SAGV7FKX/Paszke et al. - 2019 - PyTorch An Imperative Style, High-Performance Deep Learning Library.pdf;/home/lars/Zotero/storage/P3AZNUD7/1912.html}
+}
+
 @article{riahimaneshAnalysisVulnerabilitiesAttacks2017,
   title = {Analysis of Vulnerabilities, Attacks, Countermeasures and Overall Risk of the {{Automatic Dependent Surveillance-Broadcast}} ({{ADS-B}}) System},
   author = {Riahi Manesh, Mohsen and Kaabouch, Naima},
@@ -107,6 +151,20 @@
   file = {/home/lars/Zotero/storage/5SHHA862/Shi et al. - 2018 - LSTM-based Flight Trajectory Prediction.pdf}
 }
 
+@misc{teamPandasdevPandasPandas2025,
+  title = {Pandas-Dev/Pandas: {{Pandas}}},
+  shorttitle = {Pandas-Dev/Pandas},
+  author = {pandas development {team}, The},
+  year = 2025,
+  month = sep,
+  doi = {10.5281/zenodo.17229934},
+  urldate = {2025-10-16},
+  abstract = {Pandas is a powerful data structures for data analysis, time series, and statistics.},
+  howpublished = {Zenodo},
+  keywords = {data science,python},
+  file = {/home/lars/Zotero/storage/QV289HHN/17229934.html}
+}
+
 @misc{zhangPhasedFlightTrajectory2022,
   title = {Phased {{Flight Trajectory Prediction}} with {{Deep Learning}}},
   author = {Zhang, Kai and Chen, Bowen},