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Table 4 Results from the online method for both hospitals

From: A unified machine learning approach to time series forecasting applied to demand at emergency departments

algorithm period MAE MAPE (in%) algorithm period MAE MAPE (in %)
lm 1 14.27 6.7 lm 730 10.59 8.6
glmnet 1 14.31 6.8 lm 1 10.59 8.6
gbm 730 14.33 6.8 glmnet 1 10.60 8.6
lm 7 14.33 6.8 lm 7 10.62 8.6
lm 365 14.34 6.8 gbm 1 10.63 8.6
glmnet 7 14.37 6.8 glmnet 7 10.64 8.6
glmnet 365 14.38 6.8 glmnet 730 10.64 8.5
gbm 7 14.40 6.8 gbm 7 10.71 8.7
lm 60 14.47 6.8 gbm 730 10.78 8.7
glmnet 60 14.47 6.8 lm 365 10.80 8.7
glmnet 1 14.49 6.9 glmnet 365 10.82 8.7
lm 30 14.50 6.8 lm 30 10.84 8.8
glmnet 30 14.50 6.8 glmnet 30 10.84 8.8
gbm 30 14.52 6.9 lm 60 10.86 8.8
gbm 60 14.52 6.9 glmnet 60 10.87 8.8
gbm 365 14.53 6.9 rf 1 10.93 8.9
rf 1 14.55 6.9 gbm 365 10.96 8.9
glmnet 730 14.58 6.8 gbm 30 11.00 8.9
lm 730 14.60 6.9 rf 7 11.08 9.0
rf 7 14.66 6.9 gbm 60 11.15 9.0
rf 730 14.73 6.9 rf 60 11.21 9.1
rf 365 14.76 7.0 rf 30 11.21 9.1
rf 60 15.02 7.1 rf 365 11.49 9.2
rf 30 15.08 7.1 rf 730 11.51 9.0
knn 1 15.52 7.3 knn 7 12.55 10.1
knn 7 15.53 7.3 knn 1 12.60 10.1
knn 730 15.61 7.4 knn 30 12.75 10.2
knn 365 15.62 7.4 knn 60 12.78 10.2
knn 30 15.75 7.4 knn 730 12.81 10.1
knn 60 15.93 7.5 knn 365 12.81 10.1
  (a) St Mary’s Hospital     (b) Charing Cross Hospital