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An error correction neural network for stock market prediction

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Figure

Figure 2.1: Two industrial revolutions showing jump discontinuities caused by stock split
Figure 2.9: A network with four layers. The edge corresponding to the weight w 3 43 is highlighted.
Figure 3.2: The computational graph to compute the training loss of the recurrent network that maps an input into a sequence of x values to a corresponding sequence of output y values
Figure 4.2: ECNN incorporating overshooting. Note that − Id is the fixed negative of an identity matrix, while z (t−1) are outputs clusters to model the error correction mechanism (see comment in Figure 4.1).
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