Discussing the article: "Deep Learning GRU model with Python to ONNX with EA, and GRU vs LSTM models"
Can you please how you are using the same input close price to predict the same output close price? A bit confused should we not try to predict the next bars price? thanks
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Check out the new article: Deep Learning GRU model with Python to ONNX with EA, and GRU vs LSTM models.
We will guide you through the entire process of DL with python to make a GRU ONNX model, culminating in the creation of an Expert Advisor (EA) designed for trading, and subsequently comparing GRU model with LSTM model.
GRU, an acronym for Gated Recurrent Unit, represents a variant of recurrent neural network (RNN) architecture akin to LSTM (Long Short-Term Memory).
Much like LSTM, GRU is crafted for modeling sequential data, enabling selective retention or omission of information across time. Notably, GRU boasts a streamlined architecture relative to LSTM, featuring fewer parameters. This characteristic enhances ease of training and computational efficiency.
The primary distinction between GRU and LSTM lies in their handling of the memory cell state. In LSTM, the memory cell state is distinct from the hidden state and undergoes updates through three gates: the input gate, output gate, and forget gate. Conversely, GRU replaces the memory cell state with a "candidate activation vector," updated via two gates: the reset gate and update gate.
In summary, GRU emerges as a favored alternative to LSTM for sequential data modeling, particularly in scenarios where computational constraints exist or a simpler architecture is preferred.
Author: Javier Santiago Gaston De Iriarte Cabrera