A Graph-based prediction model with applications

Andras London , József Németh , Miklós Krész

Abstract

We present a new model for probabilistic forecasting using graph-based rating method. We provide a “forward-looking” type graph-based approach and apply it to predict football game outcomes by simply using the historical game results data of the investigated competition. The assumption of our model is that the rating of the teams after a game day correctly reflects the actual relative performance of them. We consider that the smaller the changing of the rating vector – contains the ratings of each team – after a certain outcome in an upcoming single game, the higher the probability of that outcome. Performing experiments on European football championships data, we can observe that the model performs well in general and outperforms some of the advanced versions of the widely-used Bradley-Terry model in many cases in terms of predictive accuracy. Although the application we present here is special, we note that our method can be applied to forecast general graph processes.
Author Andras London (WIiGE / KBO)
Andras London,,
- Department of Operations Research
, József Németh - University of Szeged, Hungary
József Németh,,
-
, Miklós Krész - University of Szeged, Hungary
Miklós Krész,,
-
Pages49-53
Publication size in sheets0.5
Book Mladenić Dunja, Grobelnik Marko (eds.): Proceedings of the 21st International Multiconference INFORMATION SOCIETY – IS 2018, Informacijska družba, vol. C, 2018, Jožef Stefan Institute, ISBN 978-961-264-137-5, 56 p.
Keywords in PolishEksploracja danych na podstawie wykresów, Prognoza, Nauczanie maszynowe
Keywords in EnglishGraph based data mining, Prediction, Machine Learning
URL http://library.ijs.si/Stacks/Proceedings/InformationSociety/2018/IS2018_Volume_C%20-%20SiKDD.pdf
Languageen angielski
Score (nominal)5
Score sourcepublisherList
ScoreMinisterial score = 5.0, 23-04-2020, ChapterFromConference
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