# Time series to images

> Time series to images - Generate advanced features used for high frequency data use cases.

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Companion generated at `2026-05-06T18:17:09.571471+00:00` (UTC).

## Primary page

- [Time series to images](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/adv-analytics-tools/gramian.html): Full documentation for this topic (HTML).

## Related documentation

- [Developer documentation](https://docs.datarobot.com/en/docs/api/index.html): Linked from this page.
- [Developer learning](https://docs.datarobot.com/en/docs/api/dev-learning/index.html): Linked from this page.
- [AI accelerators](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/index.html): Linked from this page.
- [Advanced analytics and tools](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/adv-analytics-tools/index.html): Linked from this page.

## Documentation content

[Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/audio_and_sensors-gramian_angular_fields_for_high_freq_data_to_images/high_frequency_data_classification_using_gramian_angular_fields.ipynb)

Prerequisites: [PYTS library](https://pyts.readthedocs.io/)

Traditional feature engineering methods like time aware aggregation and spectrograms can have limitations. Spectrograms cannot capture correlations between each segment of the signal with other segments of the signal. If you try to do this with tabular aggregates it becomes a high dimensionality problem.

Gramian Angular Field images of signal data can solve the above problem using a matrix which can be used with computer vision models easily without the limitations of dimensionality.