Prediction of skiing time by structured regression algorithm

In this paper, the application of Gaussian conditional random fields (GCRF) in the case of prediction skiing time between ski gates in ski center Kopaonik, is presented. Gaussian conditional random fields is well-known structured regression method that exploits advantages of unstructured predictors and combines them with the information concerning correlation between outputs. Four different unstructured predictors were used: ridge regression, LASSO regression, Random forest regression and support vector machine regression. Even thought, only 18 features are used for prediction of skiing time, GCRF achieved better results, concerning R2 and mean absolute error, compared to unstructured predictors

7th INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ENGINEERING - SIE 2018, PROCEEDINGS

editors Vesna Spasojević-Brkić, Mirjana Misita, Dragan D. Milanovi

7th INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ENGINEERING - SIE 2018, PROCEEDINGS

editors Vesna Spasojević-Brkić, Mirjana Misita, Dragan D. Milanovi