Welcome to the next step in your journey of handling multivariate time series with Recurrent Neural Networks (RNNs). In the previous lessons, you learned how to preprocess the Air Quality dataset and prepare it for RNN input. Now, we will focus on building and training an RNN model to predict Temperature (T) using multiple features from this dataset. RNNs are particularly well-suited for time series forecasting due to their ability to capture temporal dependencies in sequential data. By the end of this lesson, you will have a solid understanding of how to construct and train an RNN model for multivariate time series forecasting.

Before building the RNN model, it's essential to preprocess the Air Quality dataset to ensure it is suitable for input into the model. Here are the preprocessing steps you should have completed:

Before training the RNN model, it's important to evaluate its performance on unseen data. To do this, we split our dataset into training and test sets. For time series data, we use the earlier portion for training and the later portion for testing to preserve the temporal order.

Now, use X_train and y_train for training the model, and reserve X_test and y_test for evaluating its performance after training.

To begin, let's set up the architecture of our RNN model using PyTorch. In PyTorch, models are defined by creating a class that inherits from nn.Module. This class will include an __init__ method to define the layers and a forward method to specify the forward pass of the model.

Each input sample has the shape (sequence_length, len(features)), and the full input batch has shape (batch_size, sequence_length, len(features)) as expected by PyTorch when batch_first=True is set in the RNN layer. Here, sequence_length is the number of time steps in each sequence, and len(features) is the number of features in each time step. This matches the shape of our prepared data, ensuring compatibility with the RNN layer.

Let's walk through the code to define the RNN model using PyTorch. We will use torch.nn modules such as nn.RNN and nn.Linear to construct the model.

In this code, we define an RNNModel class that inherits from nn.Module. The __init__ method initializes an nn.RNN layer with 20 hidden units and a nn.Linear layer for the output. The forward method defines the forward pass, where the RNN processes the input sequences, and the final output is obtained from the last time step.

In PyTorch, training a model involves defining a loss function, an optimizer, and manually implementing the training loop. We will use the Adam optimizer and mean squared error (MSE) as the loss function, as they are commonly used for regression tasks.

In this code, we define the criterion as nn.MSELoss() and the optimizer as optim.Adam(). We convert the training data to PyTorch tensors and implement a training loop that iterates over the data in batches. For each batch, we perform a forward pass, compute the loss, perform a backward pass, and update the model parameters.

After training the model, it's important to evaluate its structure and performance. We can print the model structure directly to understand its configuration.

Printing the model provides a detailed overview of the model, including the layers and their configurations. This information is valuable for understanding the complexity of the model and ensuring that it is correctly configured.

In summary, you have learned how to build and train an RNN model for multivariate time series forecasting using PyTorch. We covered the model architecture, data splitting, compilation, and training process, providing you with the foundational skills needed to construct RNN models. As you move on to the practice exercises, I encourage you to apply these concepts and experiment with different parameters to deepen your understanding. Proper model building and training are key to achieving accurate time series forecasts.