Advanced Data Analytics Using Python_ With Machine Learning, Deep Learning and NLP Examples ( 2023)

Chapter 5
Deep Learning and Neural Networks
Recurrent Neural Network
A recurrent neural network is an extremely popular kind of network
where the output of the previous step goes to the feedback or is input
to the hidden layer. It is an extremely useful solution for a problem like
a sequence leveling algorithm or time-series prediction. One of the
more popular applications of the sequence leveling algorithm is in an
autocomplete feature of a search engine.
As an example, say one algorithmic trader wants to predict the price
of a stock for trading. But his strategy requires the following criteria for
prediction:
a) The predicted tick is higher than the current tick and
the next tick. Win.
b) The predicted tick is lower than the current tick and
the next tick. Win.
c) The predicted tick is higher than the current tick but
lower than the next tick. Loss.
d) The predicted tick is lower than the current tick but
higher than the next tick. Loss.
To satisfy his criteria, the developer takes the following strategy.
For generating predictions for 100 records, he is considering preceding
1,000 records as input 1, prediction errors in the last 1,000 records as input
2, and differences between two consecutive records as input 3. Using these
inputs, an RNN-based engine predicts results, errors, and inter-record
differences for the next 100 records.
Then he takes the following strategy:
If predicted diff > 1 and predicted err < 1, then
prediction += pred_err + 1.
If predicted diff < 1 and predicted err > 1, then
prediction -= pred_err -1.
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