Welcome to the final lesson in your journey of handling multivariate time series with Recurrent Neural Networks (RNNs). In the previous lesson, you learned how to build and train an RNN model to predict Temperature (T) using multiple features from the Air Quality dataset. Now, we will focus on evaluating the performance of this RNN model. Evaluating model performance is crucial as it helps you understand how well your model is making predictions and where improvements can be made. In this lesson, we will use the Root Mean Squared Error (RMSE) as our key evaluation metric. RMSE is widely used in regression tasks as it provides a measure of the differences between predicted and actual values.

Before we dive into evaluating the model, let's quickly recap the steps we took to build and train the RNN model using PyTorch, now including a train/test split for proper evaluation:

To visualize the training loss, you need to record the loss values during training and plot them using matplotlib. This helps in understanding the model's learning progress over epochs.

In this plot, you can see how the training loss decreases over the epochs, indicating the model's learning progress.

To evaluate the RNN model, we first need to make predictions on the test data. This involves using the trained model to predict the target variable, Temperature (T), based on the input features. It's important to ensure that the data used for predictions has undergone the same preprocessing steps as the training data.

The predictions generated by the model are in the scaled form, as the input data was normalized during preprocessing. To evaluate the model's performance meaningfully, we need to rescale these predictions back to their original values.

With the predictions rescaled to their original values, we can now calculate the RMSE to evaluate the model's performance.

Visualization is a powerful tool for understanding model performance. By plotting the actual and predicted values, you can visually assess how well the model is capturing the patterns in the data.

You can see the plot output here, which shows how well the predicted values align with the actual ones overall.

In this lesson, you learned how to evaluate the performance of an RNN model for multivariate time series forecasting using PyTorch. We covered splitting the data into training and test sets, making predictions, rescaling them to their original values, calculating RMSE, and visualizing the results. These steps are essential for assessing how well your model is performing and identifying areas for improvement. As you move on to the practice exercises, I encourage you to apply these concepts and experiment with different parameters to deepen your understanding.