GRUBitCoin: Bitcoin Price Prediction with GRUs

Model Details

• Model Architecture: Gated Recurrent Unit (GRU) Network
• Framework: PyTorch
• Input Shape: Time series sequences of Bitcoin price data (single feature)
• Output: Predicted Bitcoin price for the next timestep
• Dataset: Bitcoin Historical Data

Model Description

The GRUBitCoin model is a recurrent neural network (RNN) designed for Bitcoin price prediction. It utilizes a single-layer GRU with 64 hidden units followed by a fully connected feedforward network. The architecture is designed to capture temporal dependencies in historical Bitcoin price data.

Training Details

• Optimizer: Adam
• Batch Size: 64
• Loss Function: Mean Squared Error (MSE)
• Number of Epochs: 10
• Dropout: 50%
• Activation Functions: ReLU in the feedforward layers

Model Architecture

class GRUBitCoin(nn.Module, PyTorchModelHubMixin):
    def __init__(self):
        super(GRUBitCoin, self).__init__()
        self.lstm = nn.GRU(
            input_size=1, hidden_size=64, num_layers=1, batch_first=True, dropout=0.5
        )
        self.seq1 = nn.Sequential(
            nn.Flatten(),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(1920, 32),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(32, 1),
        )

    def forward(self, x):
        x, _ = self.lstm(x)
        x = self.seq1(x)
        return x.flatten()


This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Library: [More Information Needed]
- Docs: [More Information Needed]