In this lesson, we will explore how to extend Recurrent Neural Networks (RNNs) for time series classification tasks. Time series classification involves predicting categorical labels based on sequential data. We will use a dataset containing monthly airline passenger numbers to demonstrate the process of loading and preparing data, building an RNN classification model, and evaluating its performance. By the end of this lesson, you will have a solid understanding of how to apply RNNs to classify time series data.

To begin, we need to load our time series data and prepare it for classification tasks. We'll use a dataset containing monthly airline passenger numbers as an example. The first step is to load the data and preprocess it to create input sequences and corresponding labels.

In this code, we load the dataset using pandas and ensure the column names match. We generate binary labels indicating whether the next value in the time series is higher or lower before scaling the data. We then normalize the passenger numbers to a range between 0 and 1 using MinMaxScaler. The function create_sequences generates input sequences of a specified length (seq_length) and their corresponding labels, returning the input sequences X and the target values y.

Next, we prepare the data specifically for classification by converting the labels to a categorical format.

Here, we convert the binary labels to a categorical format using to_categorical, which is necessary for training the classification model.

With our data prepared, we can now define the RNN model for classification.

We define a Sequential model with an Input layer specifying the shape of the input data, followed by a SimpleRNN layer with 20 units and a ReLU activation function, and a Dropout layer to help prevent overfitting. Another SimpleRNN layer with 10 units is added, followed by a Dense layer with 2 units and a softmax activation function. The softmax function is used to convert the output into a probability distribution over the two classes (up or down), ensuring that the sum of the probabilities is 1. We compile the model using the Adam optimizer and categorical_crossentropy loss function. The categorical_crossentropy loss function is used for multi-class classification problems. It measures the dissimilarity between the true label distribution and the predicted probability distribution. By minimizing this loss, the model learns to assign higher probabilities to the correct classes, improving its classification accuracy.

Finally, we train and evaluate the model using the prepared data.

In this section, we train and evaluate the RNN classification model. We use the fit method to train the model with the input sequences X and the categorical labels y_classification. A ReduceLROnPlateau callback is included to reduce the learning rate if no improvement in the loss is observed, which can help in achieving better convergence.

After training, we use the predict method to obtain class probabilities for the input sequences. The argmax function is then applied to these probabilities to determine the predicted class labels (y_pred_labels), selecting the class with the highest probability for each sequence.

We compute the model's accuracy by comparing the predicted labels with the actual labels (y). Accuracy is a key metric that represents the proportion of correct predictions made by the model. It is calculated as the number of correct predictions divided by the total number of predictions. A higher accuracy indicates better model performance in classifying the time series data. Finally, we print the final model accuracy as a percentage to provide a clear understanding of the model's effectiveness.

In this lesson, we covered the process of extending RNNs for time series classification tasks. We began by loading and preparing the airline passenger dataset, creating input sequences and binary labels for classification. We then built an improved RNN classification model using SimpleRNN and Dropout layers and trained it on the prepared data. Finally, we evaluated the model's performance by predicting class labels and calculating its accuracy. This lesson provided a comprehensive overview of using RNNs for time series classification, equipping you with the skills to apply these techniques to your own datasets.