GMS location: 554

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.989 0.000e+00 0.325 0.430 1.954 NaN NaN
forest winter 2016 0.989 0.000e+00 0.264 0.386 1.722 0.473 5.293
baseline winter 2017 0.976 0.000e+00 0.325 0.421 2.085 NaN NaN
forest winter 2017 0.984 0.000e+00 0.218 0.340 1.587 0.483 4.491
baseline winter 2018 0.994 0.000e+00 0.284 0.410 2.054 NaN NaN
forest winter 2018 1.000 0.000e+00 0.218 0.344 2.205 0.473 4.067
baseline winter 2019 0.994 0.000e+00 0.263 0.382 1.626 NaN NaN
forest winter 2019 0.994 0.083 0.227 0.352 1.523 0.474 4.418
baseline all 0.989 0.000e+00 0.299 0.411 2.085 NaN NaN
forest all 0.992 0.014 0.233 0.356 2.205 0.475 4.589

Random forest plots

My plot :) My plot :) My plot :) My plot :) My plot :) My plot :) My plot :) My plot :) My plot :) My plot :)

Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.989 0.000e+00 0.325 0.430 1.954 NaN NaN
elr winter 2016 0.989 0.000e+00 0.280 0.399 1.790 0.542 6.207
baseline winter 2017 0.976 0.000e+00 0.325 0.421 2.085 NaN NaN
elr winter 2017 0.984 0.000e+00 0.298 0.386 2.090 0.534 5.832
baseline winter 2018 0.994 0.000e+00 0.284 0.410 2.054 NaN NaN
elr winter 2018 0.987 0.000e+00 0.232 0.345 2.179 0.524 5.500
baseline winter 2019 0.994 0.000e+00 0.263 0.382 1.626 NaN NaN
elr winter 2019 1.000 0.083 0.264 0.383 1.803 0.501 4.528
baseline all 0.989 0.000e+00 0.299 0.411 2.085 NaN NaN
elr all 0.990 0.014 0.268 0.378 2.179 0.526 5.539

Extended logistic regression plots

My plot :) My plot :) My plot :) My plot :) My plot :) My plot :) My plot :) My plot :) My plot :) My plot :)