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Table 8 Comparative characteristics of neural networks

From: Bitcoin fluctuations and the frequency of price overreactions

Architecture Performance Errors
Learning Control Test Learning Control Test
MLP 2-2-3-1:1 0.4484 0.4547 0.5657 0.0811 0.0392 0.0630
L 2-2-1:1 0.3809 0.6265 0.8314 0.0664 0.0801 0.0836
  1. This table presents comparative characteristics of the neural networks from the multilayer perceptron (MLP) method. The first column reports the architecture of the network models, the second, third and fourth columns show the estimates for the performance parameters of the models learning, control and test, respectively; the fifth, sixth and seventh columns show the estimates for errors of the models learning, control and test, respectively