Aeon: A unified framework for machine learning with time series

Aeon is a unified framework for machine learning with time series data. It is an open-source project developed by aeon-toolkit and is licensed under the BSD-3-Clause license. Aeon provides a simple and intuitive interface for working with time series data, making it easier for developers to build machine learning models for time series analysis.

One of the key features of Aeon is its ability to handle both univariate and multivariate time series data. It also provides a variety of tools for preprocessing, visualization, and analysis of time series data. Aeon's modular architecture allows developers to easily extend and customize the framework to fit their specific needs.

Aeon supports a wide range of machine learning algorithms, including deep learning models such as LSTM and GRU. It also provides a number of evaluation metrics for assessing the performance of machine learning models on time series data.

To get started with Aeon, developers can install it using pip and then import it into their Python code. Here's an example of how to use Aeon for time series forecasting:

from aeon import TimeSeries
from aeon.models import LSTM

# Load time series data
data = TimeSeries.load_csv('data.csv')

# Split data into training and test sets
train_data, test_data = data.split_train_test(0.8)

# Train LSTM model
model = LSTM()
model.fit(train_data)

# Make predictions on test data
predictions = model.predict(test_data)

# Evaluate model performance
mse = model.evaluate(test_data)
print('Mean Squared Error:', mse)

Overall, Aeon is a powerful and flexible framework for machine learning with time series data. Its user-friendly interface and extensive documentation make it a great choice for developers looking to build machine learning models for time series analysis.