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Table 3 Errors of prediction models

From: Characterizing financial markets from the event driven perspective

Model W News Finance News + Finance
   MAE MSE MAE MSE MAE MSE
Decision tree cent 0.775 35.091 0.717 3.375 0.687 3.267
  clst 0.768 35.076 0.717 3.375 0.719 3.322
  cos 0.809 35.187 0.717 3.375 0.666 3.280
  count 0.710 34.853 0.717 3.375 0.727 3.402
Gaussian process cent 0.587 34.314 0.570 2.699 0.566 2.689
  clst 0.587 34.311 0.570 2.699 0.571 2.703
  cos 0.586 34.309 0.570 2.699 0.567 2.706
  count 0.589 34.312 0.570 2.699 0.565 2.730
KernelRidge cent 2.347 50.602 0.611 3.084 1.243 7.191
  clst 1.981 42.257 0.611 3.084 2.478 18.887
  cos 2.743 57.785 0.611 3.084 1.242 11.319
  count 1.227 35.929 0.611 3.084 2.709 32.986
Linear regression cent 2.644 55.001 0.612 3.096 3.244 40.500
  clst 2.932 62.219 0.612 3.096 4.134 57.534
  cos 2.969 62.224 0.612 3.096 3.391 62.338
  count 2.967 65.251 0.612 3.096 3.291 73.918
Nearest neighbors cent 0.559 34.293 0.661 2.844 0.618 2.694
  clst 0.579 34.403 0.661 2.844 0.642 2.796
  cos 0.542 34.375 0.661 2.844 0.617 2.779
  count 0.562 34.379 0.661 2.844 0.667 2.797
Neural net cent 0.801 35.289 1.513 21.822 0.700 3.587
  clst 0.922 36.004 1.491 20.703 0.743 3.726
  cos 0.730 34.990 1.493 20.175 0.755 4.007
  count 1.150 36.867 1.482 20.203 0.943 5.766
Random forest cent 0.699 34.401 0.660 2.880 0.627 2.804
  clst 0.697 34.397 0.660 2.880 0.638 2.695
  cos 0.697 34.416 0.660 2.880 0.635 2.904
  count 0.662 34.325 0.660 2.880 0.662 2.861
  1. This table presents mean absolute error (MAE) and mean squared error (MSE) of various models on variations of two input data sets, historical financial time series (Finance) and network obtained from the news events (News). Last column represents errors when both data sets were used for training of the models and bold numbers show the best performing model on each data set