init
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28
forecasting_model/model.py
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28
forecasting_model/model.py
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import torch
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import torch.nn as nn
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from typing import Optional
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from utils.config_model import ModelConfig
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class LSTMForecastModel(nn.Module):
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def __init__(self, model_config: ModelConfig):
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super().__init__()
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self.config = model_config
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self.use_residual_skips = model_config.use_residual_skips
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# TODO: Initialize LSTM layers
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# TODO: Initialize dropout
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# TODO: Initialize output layer
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# TODO: Initialize residual connection layer if needed
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""
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Forward pass through the LSTM network.
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Args:
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x: Input tensor of shape (batch_size, sequence_length, input_size)
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Returns:
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Predictions tensor of shape (batch_size, forecast_horizon)
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"""
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# TODO: Implement forward pass with optional residual connections
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pass
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