Welcome back! In the previous lesson, we explored the basics of Recurrent Neural Networks (RNNs) and time series data. We discussed how RNNs are uniquely suited for handling sequential data due to their ability to retain information from previous inputs. We also visualized time series data to identify patterns and trends. Now, we will build on that foundation by focusing on preparing time series data specifically for RNNs. This lesson will guide you through the process of normalizing, standardizing, and converting time series data into sequences, which are essential steps for training RNN models effectively.

Before feeding data into an RNN, it is crucial to normalize or standardize it. These preprocessing steps ensure that all input features are on a similar scale. If you skip normalization or standardization, your RNN may encounter several issues:

• Slower Training: Features with larger values can dominate the learning process, making it harder for the model to learn from features with smaller values. This can slow down convergence during training.
• Unstable Gradients: RNNs are sensitive to the scale of input data. Without normalization or standardization, the gradients during backpropagation can become very large or very small (exploding or vanishing gradients), making training unstable or causing the model to fail to learn.
• Poor Model Performance: The model may not learn the underlying patterns in the data effectively, leading to lower accuracy and worse predictions.

By normalizing or standardizing your data, you help the RNN learn more efficiently and achieve better results. This is why these preprocessing steps are considered best practices when working with neural networks, especially for time series data.

Normalization scales the data to a specific range, typically between 0 and 1, which helps improve the model's performance and convergence speed. The MinMaxScaler from the sklearn.preprocessing module performs this scaling using the following formula:

In addition to normalization, another common preprocessing step is standardization. Standardization transforms the data so that it has a mean of 0 and a standard deviation of 1. This is especially useful when your data has varying scales or units, or when the model's performance benefits from having zero-centered data. Standardization can help the RNN converge faster and avoid issues where features with larger scales dominate the learning process.

The standardization formula is:

RNNs require input data to be in the form of sequences. A sequence is a series of data points that the RNN processes one at a time. To convert our time series data into sequences, we need to define a sequence length, which determines how many previous data points the model will consider at each step.

Let's create a function to convert our normalized or standardized time series data into sequences:

In this function, create_sequences, we iterate over the data to create input sequences X and corresponding target values y. Each sequence consists of seq_length data points, and the target value is the data point immediately following the sequence. This setup allows the RNN to learn patterns and make predictions based on past observations.

Once we have our sequences, we need to reshape them to fit the input requirements of an RNN. RNNs expect input data to have three dimensions: the number of samples, the sequence length, and the number of features.

• Number of samples: This is the total number of input sequences in your dataset. Each sample is one sequence that the RNN will process independently.
• Sequence length: This is the number of time steps in each input sequence. It determines how many previous data points the RNN will use to make a prediction.
• Number of features: This is the number of variables or measurements at each time step. For a univariate time series (like our 'Passengers' example), this is 1. For a multivariate time series (with multiple variables per time step), this would be greater than 1.

For example, if you have 100 sequences, each of length 10, and each time step contains 1 feature, your input shape will be (100, 10, 1).

Here's how you can reshape the sequences using PyTorch:

This line of code reshapes the input sequences X to have three dimensions:

• The first dimension represents the number of samples (sequences),
• The second dimension is the sequence length (number of time steps),
• The third dimension is the number of features (variables per time step, which is 1 in this case).

Reshaping the data correctly is crucial for the RNN to process it effectively.

Let's walk through the entire process of preparing a sample dataset for RNNs. We will start with a dataset containing the number of airline passengers, normalize or standardize the data, convert it into sequences, and reshape it for RNN input.

First, we normalize or standardize the 'Passengers' column using MinMaxScaler or StandardScaler. Next, we use the create_sequences function to generate input sequences and target values. Finally, we reshape the input sequences to fit the RNN's input requirements. Here's the complete code:

By following these steps, you have successfully prepared your time series data for training an RNN. This process is essential for ensuring that the model can learn from the data and make accurate predictions.

In this lesson, we focused on preparing time series data for RNNs. We discussed the importance of data normalization and standardization, demonstrating how to use MinMaxScaler and StandardScaler to scale the data. We explained why these steps are important for RNN training: they help the model train faster, avoid unstable gradients, and achieve better performance. We then converted the normalized or standardized data into sequences, which are necessary for RNNs to learn temporal patterns. Finally, we reshaped the sequences to fit the input requirements of an RNN using PyTorch.

As you move on to the practice exercises, you'll have the opportunity to apply these techniques to your own datasets. Hands-on practice is crucial for reinforcing the concepts learned in this lesson. In the next unit, we will delve deeper into building and evaluating a basic RNN model. Keep up the great work, and happy learning!