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 it. Normalization scales the data to a specific range, typically between 0 and 1, which helps improve the model's performance and convergence speed. In this lesson, we will use the MinMaxScaler from the sklearn.preprocessing module to normalize our data. This tool is pre-installed in the CodeSignal environment, so you can focus on understanding its application.

Let's consider a dataset containing the number of airline passengers over time. We will normalize the 'Passengers' column using MinMaxScaler. Here's how you can do it:

In this code, we first import the necessary libraries. We then create an instance of MinMaxScaler and apply it to the 'Passengers' column of our dataset. The fit_transform method scales the data to the range [0, 1]. This step ensures that all features contribute equally to the model's learning process.

In addition to normalization, another common preprocessing step is standardization. Standardization scales the data to have a mean of 0 and a standard deviation of 1. This can be particularly useful when the data has varying scales or when the model's performance benefits from having zero-centered data. We will use the StandardScaler from the sklearn.preprocessing module to standardize our data.

Here's how you can standardize the 'Passengers' column:

In this code, we create an instance of StandardScaler and apply it to the 'Passengers' column of our dataset. The fit_transform method standardizes the data to have a mean of 0 and a standard deviation of 1. This step can help improve the model's performance, especially when the input features have different units or scales.

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. In our case, each data point is a single feature.

Here's how you can reshape the sequences:

This line of code reshapes the input sequences X to have three dimensions. The first dimension represents the number of samples, the second dimension is the sequence length, and the third dimension is the number of features, 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 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.

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!