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Table 5 MAE error rates for all types of hyperparameter tuning predicting hospital demand 1, 3 or 7 days in advance in case of the batch method for Charing Cross hospital

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

   Choosing the best set of hyperparameters based on:
algorithm days yesterday the past exponential the average over the caret
    n days moving average whole training set  
  1 10.66 10.60 10.67 10.58 10.51
lm 3 10.75 10.72 10.84 10.69 10.69
  7 10.78 10.90 10.83 10.87 10.77
  1 12.50 10.68 11.12 10.68 10.89
gbm 3 12.49 10.58 10.92 10.50 10.78
  7 12.11 10.73 11.03 10.57 10.72
  1 10.72 10.51 10.59 10.51 10.51
glmnet 3 10.85 10.69 10.74 10.69 10.69
  7 10.86 10.76 10.84 10.76 10.76
  1 12.81 12.57 12.62 12.77 12.36
knn 3 12.85 12.58 12.78 12.87 12.51
  7 12.65 12.45 12.41 12.47 12.33
  1 11.09 10.84 11.04 11.55 10.89
rf 3 10.98 10.80 10.83 11.85 10.78
  7 10.92 10.78 10.79 11.99 10.72