The 2023/24 VIEWS Prediction Challenge: Predicting the Number of Fatalities in Armed Conflict, with Uncertainty
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| Format: | Preprint |
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2024
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| _version_ | 1866929421841072128 |
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| author | Hegre, Håvard Vesco, Paola Colaresi, Michael Vestby, Jonas Timlick, Alexa Kazmi, Noorain Syed Becker, Friederike Binetti, Marco Bodentien, Tobias Bohne, Tobias Brandt, Patrick T. Chadefaux, Thomas Drauz, Simon Dworschak, Christoph D'Orazio, Vito Fritz, Cornelius Frank, Hannah Gleditsch, Kristian Skrede Häffner, Sonja Hofer, Martin Klebe, Finn L. Macis, Luca Malaga, Alexandra Mehrl, Marius Metternich, Nils W. Mittermaier, Daniel Muchlinski, David Mueller, Hannes Oswald, Christian Pisano, Paola Randahl, David Rauh, Christopher Rüter, Lotta Schincariol, Thomas Seimon, Benjamin Siletti, Elena Tagliapietra, Marco Thornhill, Chandler Vegelius, Johan Walterskirchen, Julian |
| author_facet | Hegre, Håvard Vesco, Paola Colaresi, Michael Vestby, Jonas Timlick, Alexa Kazmi, Noorain Syed Becker, Friederike Binetti, Marco Bodentien, Tobias Bohne, Tobias Brandt, Patrick T. Chadefaux, Thomas Drauz, Simon Dworschak, Christoph D'Orazio, Vito Fritz, Cornelius Frank, Hannah Gleditsch, Kristian Skrede Häffner, Sonja Hofer, Martin Klebe, Finn L. Macis, Luca Malaga, Alexandra Mehrl, Marius Metternich, Nils W. Mittermaier, Daniel Muchlinski, David Mueller, Hannes Oswald, Christian Pisano, Paola Randahl, David Rauh, Christopher Rüter, Lotta Schincariol, Thomas Seimon, Benjamin Siletti, Elena Tagliapietra, Marco Thornhill, Chandler Vegelius, Johan Walterskirchen, Julian |
| contents | This draft article outlines a prediction challenge where the target is to forecast the number of fatalities in armed conflicts, in the form of the UCDP `best' estimates, aggregated to the VIEWS units of analysis. It presents the format of the contributions, the evaluation metric, and the procedures, and a brief summary of the contributions. The article serves a function analogous to a pre-analysis plan: a statement of the forecasting models made publicly available before the true future prediction window commences. More information on the challenge, and all data referred to in this document, can be found at https://viewsforecasting.org/research/prediction-challenge-2023. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_11045 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The 2023/24 VIEWS Prediction Challenge: Predicting the Number of Fatalities in Armed Conflict, with Uncertainty Hegre, Håvard Vesco, Paola Colaresi, Michael Vestby, Jonas Timlick, Alexa Kazmi, Noorain Syed Becker, Friederike Binetti, Marco Bodentien, Tobias Bohne, Tobias Brandt, Patrick T. Chadefaux, Thomas Drauz, Simon Dworschak, Christoph D'Orazio, Vito Fritz, Cornelius Frank, Hannah Gleditsch, Kristian Skrede Häffner, Sonja Hofer, Martin Klebe, Finn L. Macis, Luca Malaga, Alexandra Mehrl, Marius Metternich, Nils W. Mittermaier, Daniel Muchlinski, David Mueller, Hannes Oswald, Christian Pisano, Paola Randahl, David Rauh, Christopher Rüter, Lotta Schincariol, Thomas Seimon, Benjamin Siletti, Elena Tagliapietra, Marco Thornhill, Chandler Vegelius, Johan Walterskirchen, Julian Applications Machine Learning Computation J.4; I.6.3; I.6.4; I.6.5 This draft article outlines a prediction challenge where the target is to forecast the number of fatalities in armed conflicts, in the form of the UCDP `best' estimates, aggregated to the VIEWS units of analysis. It presents the format of the contributions, the evaluation metric, and the procedures, and a brief summary of the contributions. The article serves a function analogous to a pre-analysis plan: a statement of the forecasting models made publicly available before the true future prediction window commences. More information on the challenge, and all data referred to in this document, can be found at https://viewsforecasting.org/research/prediction-challenge-2023. |
| title | The 2023/24 VIEWS Prediction Challenge: Predicting the Number of Fatalities in Armed Conflict, with Uncertainty |
| topic | Applications Machine Learning Computation J.4; I.6.3; I.6.4; I.6.5 |
| url | https://arxiv.org/abs/2407.11045 |