Dominic Kneup

Time Series Forecasting with Concurrent Neural Networks


Time Series Forecasting is a critical task across many industries, from finance to healthcare, where accurately predicting future data points based on past observations can inform decision-making processes. Convolutional Neural Networks (CNNs), while traditionally associated with image processing, have shown promise in handling sequential data, including natural language processing (NLP), audio processing, and more recently, time series forecasting.

In this article, we will discuss how CNNs can be adapted for time series forecasting, compare them with traditional RNN and LSTM models, and provide practical code examples in TensorFlow. We’ll also dive into the limitations, common pitfalls, and the importance of hyperparameter tuning for optimal results.

Table of Contents

  1. Why Use CNNs for Time Series Forecasting?
  2. CNN Architecture for Time Series Forecasting
  3. CNN vs. RNN/LSTM for Time Series Forecasting
  4. Practical Example: Time Series Forecasting with CNNs in TensorFlow
  5. Best Practices for Using CNNs in Time Series Forecasting
  6. Common Pitfalls to Avoid
  7. Conclusion

1. Why Use CNNs for Time Series Forecasting?

CNNs can capture temporal dependencies by extracting features from fixed-length sequences, and they excel at recognizing local patterns in the data. Unlike RNNs and LSTMs, which sequentially process data, CNNs can process data in parallel, leading to faster computation. However, it’s worth noting that while CNNs can handle short-term dependencies well, their ability to capture long-term dependencies is more limited compared to RNNs and LSTMs.

Benefits of CNNs for Time Series Forecasting:

  1. Efficient Parallelism: CNNs can process data in parallel, making them computationally efficient compared to RNNs or LSTMs, which process data sequentially.
  2. Local Feature Extraction: CNNs are particularly good at extracting short-term patterns from time series data using convolutional filters.
  3. Reduced Complexity: CNNs often require fewer parameters than RNNs or LSTMs, which reduces the risk of overfitting and speeds up training.

2. CNN Architecture for Time Series Forecasting

The architecture of a CNN for time series forecasting typically involves applying convolutional layers to a fixed-length input window of past observations. These convolutional layers help capture local patterns in the time series data. For multivariate time series, the input shape is a 3D array, where one dimension represents samples, another represents time steps, and the final dimension represents features.

Key Components of CNN Architecture:

  • Input Shape: For univariate time series data, the input shape is a 3D array of shape (samples, time steps, 1 feature). For multivariate time series data, it is (samples, time steps, features), allowing CNNs to handle datasets with multiple variables.
  • Convolutional Layers: These layers apply filters to extract patterns from the time series, with kernel sizes determining the scope of the temporal dependencies captured.
  • Pooling Layers: Pooling is used to reduce the dimensionality of the feature maps and focus on the most important information.
  • Fully Connected Layers: Dense layers combine the extracted features to make predictions.

Handling Multivariate Time Series:

For multivariate time series, the input will have multiple features per time step. For instance, when predicting stock prices, you might include features like stock prices, trading volume, and market sentiment as inputs. Each feature contributes to the convolutional layers, allowing the model to learn relationships between them.

Example of Preparing Multivariate Time Series Data:

# Assume 'data' is a NumPy array of shape (samples, features)
# We will create sequences using a sliding window approach

def create_multivariate_dataset(data, window_size):
    x, y = [], []
    for i in range(len(data) - window_size):
        x.append(data[i:i+window_size])
        y.append(data[i+window_size])  # Predicting the next time step
    return np.array(x), np.array(y)

# Example usage
window_size = 10
x, y = create_multivariate_dataset(multivariate_data, window_size)
# x.shape will be (samples, time steps, features)

3. CNN vs. RNN for Time Series Forecasting

3.1 CNN Advantages

  • Speed: CNNs can process data faster than RNNs and LSTMs due to their parallel nature.
  • Local Feature Extraction: CNNs can efficiently extract features from short-term patterns, and by stacking convolutional layers, they can capture longer-range dependencies to some extent.
  • Simpler Models: CNNs typically have fewer parameters than RNNs and LSTMs, which can reduce overfitting.

3.2 RNN/LSTM Advantages

  • Modeling Long-Term Dependencies: RNNs and LSTMs are specifically designed to handle long-term dependencies in time series data. LSTMs, in particular, mitigate the vanishing gradient problem through their gating mechanisms.
  • Sequential Nature: The recurrent nature of RNNs and LSTMs allows them to remember information over time, making them ideal for tasks with long temporal dependencies.

3.3 Hybrid Models

Combining CNNs with RNNs or LSTMs can leverage the strengths of both architectures. CNNs can extract local features, and RNNs/LSTMs can model the temporal dependencies over longer sequences.

4. Practical Example: Time Series Forecasting with CNNs in TensorFlow

4.1 Preparing the Data

import numpy as np
import tensorflow as tf
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt

# Set random seed for reproducibility
np.random.seed(42)

# Generate synthetic time series data (e.g., temperature data)
time_steps = 1000
time = np.arange(0, time_steps)
temperature = np.sin(time * 0.1) + np.random.normal(0, 0.1, time_steps)

# Normalize the data
scaler = MinMaxScaler(feature_range=(0, 1))
temperature_scaled = scaler.fit_transform(temperature.reshape(-1, 1))

# Prepare the data for training (window of 10 time steps)
def create_dataset(data, window_size):
    x, y = [], []
    for i in range(len(data) - window_size):
        x.append(data[i:i+window_size])
        y.append(data[i+window_size])
    return np.array(x), np.array(y)

window_size = 10
x, y = create_dataset(temperature_scaled, window_size)

# Reshape input to be [samples, time steps, features]
x = np.reshape(x, (x.shape[0], x.shape[1], 1))  # 1 feature for univariate data

# Split data into training and testing sets while maintaining temporal order
split_fraction = 0.8
split_index = int(len(x) * split_fraction)
x_train, x_test = x[:split_index], x[split_index:]
y_train, y_test = y[:split_index], y[split_index:]

Explanation:

  • We generate synthetic temperature data and normalize it using MinMaxScaler to bring all values into the range (0, 1).
  • We use a sliding window approach to create the dataset, where each window of 10 time steps is used to predict the next value.
  • The data is reshaped to be compatible with the Conv1D layer, which expects a 3D input.
  • We split the data into training and testing sets, ensuring the temporal order is preserved by not shuffling the data.

4.2 Defining the CNN Model

# Build a simple CNN model for time series forecasting
model = tf.keras.Sequential([
    tf.keras.layers.Conv1D(
        filters=64,
        kernel_size=3,
        activation='relu',
        input_shape=(window_size, 1)
    ),  # Extracts features using convolutional filters
    tf.keras.layers.MaxPooling1D(pool_size=2),  # Reduces dimensionality
    tf.keras.layers.Flatten(),  # Flattens output for the dense layers
    tf.keras.layers.Dense(50, activation='relu'),  # Dense layer with 50 units
    tf.keras.layers.Dense(1)  # Output layer for regression
])

# Compile the model
model.compile(optimizer='adam', loss='mean_squared_error')

# Summary of the model
model.summary()

# Train the model
history = model.fit(
    x_train, y_train,
    epochs=20,
    batch_size=32,
    validation_data=(x_test, y_test)
)

Model Explanation:

  • Conv1D Layer: Extracts features from the input sequence by applying convolutional filters.
  • MaxPooling1D Layer: Reduces the dimensionality and focuses on the most significant features.
  • Flatten Layer: Converts the 2D output of the previous layer into a 1D array for the Dense layers.
  • Dense Layers: Perform regression to predict the target value.

4.3 Evaluating the Model

# Generate predictions on the test set
predictions = model.predict(x_test)

# Inverse transform the predictions and true values to the original scale
predictions = scaler.inverse_transform(predictions)
y_test_inv = scaler.inverse_transform(y_test)

# Time indices for the test set
test_time = time[split_index + window_size:]

# Plot the results
plt.figure(figsize=(12, 6))
plt.plot(test_time, temperature[split_index + window_size:], label='True')
plt.plot(test_time, predictions.flatten(), label='Predicted')
plt.xlabel('Time')
plt.ylabel('Temperature')
plt.legend()
plt.show()

Explanation:

  • We generate predictions on the test set and inverse transform them to the original scale.
  • We plot the true vs. predicted temperature values for the test set.

5. Best Practices for Using CNNs in Time Series Forecasting

  1. Tune Hyperparameters: Experiment with different kernel sizes, numbers of filters, learning rates, and other parameters to find the optimal configuration.

  2. Handling Overfitting:

    • Regularization Techniques:
      • Dropout Layers: Add dropout layers to prevent overfitting. 
        tf.keras.layers.Dropout(0.2)
      • L2 Regularization: Apply L2 regularization to the weights.
    • Early Stopping: Use early stopping to halt training when validation loss stops improving. 
      early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5)

  3. Choose Appropriate Window Size: The window size significantly impacts the model’s ability to capture patterns. Experiment with different window sizes based on the nature of your data.

  4. Normalization: Always normalize or standardize the input data before feeding it into the model. CNNs can be sensitive to unscaled data.

  5. Experiment with Hybrid Models: Combining CNNs with RNNs or LSTMs can help capture both short-term and long-term dependencies.

  6. Learning Rate Scheduling: Adjust the learning rate during training for better convergence.

    lr_scheduler = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3)

6. Common Pitfalls to Avoid

  1. Using Too Small Batch Sizes: Ensure batch sizes are large enough for stable updates, especially when using batch normalization.

  2. Ignoring Data Normalization: Forgetting to normalize or standardize the time series data can lead to poor model performance.

  3. Overfitting on Small Datasets: Apply dropout and early stopping to combat overfitting on small datasets.

  4. Not Tuning Hyperparameters: Use tools like Grid Search or Random Search to find the best configuration.

  5. Ignoring Autocorrelation: Analyze the autocorrelation of the time series to understand temporal dependencies and adjust the window size accordingly.

  6. Inappropriate Loss Functions: Ensure that the loss function matches the problem type. For example, mean absolute error (MAE) might be more robust to outliers than mean squared error (MSE).

7. Conclusion

CNNs provide an efficient and powerful approach for time series forecasting, especially for datasets with short-term dependencies. While they are generally faster and simpler to train than RNNs and LSTMs, they may be less effective at capturing long-term dependencies. Combining CNNs with RNNs or LSTMs can leverage the strengths of both architectures.

Remember, the key to success in time series forecasting lies in hyperparameter tuning, proper data preprocessing, and regularization techniques. Experiment with different architectures, including hybrid models, to find the optimal solution for your forecasting task.

© 2024 Dominic Kneup