FAQ - Time Series

Where can I find more information about the models/metrics?

Metrics are implemented in the autogluon.timeseries.evaluator module. We also follow some of the same conventions followed by GluonTS in their evaluation. Please refer to the GluonTS documentation and github for further information.

A detailed description of evaluation metrics is also available at here.

How can I get the most accurate forecast predictions?

Generally setting the predictor.fit() argument presets="best_quality" or presets="high_quality" will result in high accuracy. Alternative options include manually specifying hyperparameter search spaces for certain models and manually increasing the number of hyperparameter optimization trials.

Can I use GPUs for model training?

Yes! Most of the deep learning models used by autogluon.timeseries support GPU training. PyTorch models will have GPU enabled by default. If you also want to use MXNet models, make sure you have installed CUDA and the GPU version of MXNet. Multi-GPU training is not yet supported.

What machine is best for running autogluon.timeseries?

autogluon.forecasting can be run on any machine including your laptop. Currently it is not necessary to use a GPU to train forecasting models so CPU machines are fine albeit slower for certain models. We recommend running on a machine with as much memory as possible (for instance if using AWS EC2, we recommend P3 instances) for GPU support or M6 instances for CPU training.

Issues not addressed here

First search if your issue is addressed in the tutorials, documentation, or Github issues (search both Closed and Open issues). If it is not there, please open a new Github Issue and clearly state your issue and clarify how it relates to the module.

If you have a bug, please include: your code (ideally set verbosity=4 which will print out more details), the output printed during the code execution, and information about your operating system, Python version, and installed packages (output of pip freeze). Many user issues stem from incorrectly formatted data, so please describe your data as clearly as possible.