Aeon Time Series ML FAQs

Question 1

Is deep learning included in this skill's capabilities?

Accepted Answer

Yes, the Aeon skill provides patterns for various deep learning networks, including FCN, ResNet, and InceptionTime, which are specifically tailored for temporal data.

Question 2

What makes Aeon different from standard scikit-learn?

Accepted Answer

While scikit-learn is optimized for general tabular data, Aeon provides specialized algorithms and distance metrics (like DTW) specifically designed to handle the temporal dependencies and sequential nature of time series data.

Question 3

Can I use this skill for forecasting future values?

Accepted Answer

Yes, Aeon includes a comprehensive forecasting module that ranges from traditional statistical methods like ARIMA to modern neural network architectures.

Question 4

Does Aeon support multivariate time series data?

Accepted Answer

Absolutely. Aeon is built to handle both univariate (single variable) and multivariate (multiple variables per time point) time series for classification, regression, and clustering.

Question 5

How do I integrate Aeon into an existing scikit-learn pipeline?

Accepted Answer

Because Aeon follows the scikit-learn estimator API, you can seamlessly use its transformers and models inside standard sklearn Pipeline objects for preprocessing and model evaluation.

Aeon Time Series ML

Key Features

Use Cases

Aeon Time Series ML

Key Features

Use Cases