.. _sec_forecastingfaq:

FAQ - Forecasting
=================


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

Most of the models and metrics are implemented via the `GluonTS
package <https://ts.gluon.ai/>`__, so please refer to their
`documentation <https://ts.gluon.ai/api/gluonts/gluonts.html>`__,
`github <https://github.com/awslabs/gluon-ts>`__, and paper: `GluonTS:
Probabilistic and Neural Time Series Modeling in
Python <https://www.jmlr.org/papers/v21/19-820.html>`__

A description of certain evaluation metrics is available
`here <https://docs.aws.amazon.com/forecast/latest/dg/metrics.html#metrics-wQL>`__.

How can I get the most accurate forecast predictions?
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Generally setting the ``predictor.fit()`` argument
``presets="best_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, as demonstrated in :ref:`sec_forecastingindepth`.

What are common data formatting issues?
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Each (index, time) combination must only be associated with a single
target value in your dataset, you cannot include multiple observations
of the same time-series at the same time point. The time values in
``time_column`` must be in a valid `pandas time-series
format <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html>`__
and must be uniformly spaced (eg. every hour, every day, etc.) such that
the
```pandas.infer_freq`` <https://pandas.pydata.org/docs/reference/api/pandas.infer_freq.html>`__
function can be successfully applied to these time values. Missing
target values should be in a format compatible with
`pandas <https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html>`__.
Ideally each index will be observed over the same time points, but if
some observations are missing for a particular (index, time)
combination, then you can add these to your dataset with target value NA
(AutoGluon will otherwise automatically do this padding for you). The
ordering of the rows in your dataset does not matter (AutoGluon's
forecasting models rely on the ``time_column`` value, not the row
ordering).

Can I use GPUs for model training?
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Yes! Most of the models used by AutoGluon-Forecasting support GPU
training, but it is not required that you train on a GPU. Make sure you
have installed CUDA and the GPU version of MXNet, refer to the
`installation instructions <../../install.html>`__ for more details.
AutoGluon will try to automatically detect whether your machine has a
properly setup GPU, but you can also manually specify that GPU should be
used by setting the ``predictor.fit()`` argument:
``ag_args_fit={'num_gpus': 1}``. Multi-GPU training is not yet
supported.

What machine is best for running AutoGluon-Forecasting?
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

As an open-source library, 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 and the best available GPU (for instance if using AWS
EC2, we recommend `P3
instances <https://aws.amazon.com/ec2/instance-types/p3/>`__).

Issues not addressed here
~~~~~~~~~~~~~~~~~~~~~~~~~

First search if your issue is addressed in the
`tutorials <index.html>`__,
`examples <https://github.com/awslabs/autogluon/tree/master/examples/forecasting>`__,
`documentation <../../api/autogluon.predictor.html>`__, or `Github
issues <https://github.com/awslabs/autogluon/issues>`__ (search both
Closed and Open issues). Also look through the `GluonTS Github
issues <https://github.com/awslabs/gluon-ts/issues>`__. If it is not
there, please open a `new Github
Issue <https://github.com/awslabs/autogluon/issues/new>`__ and clearly
state your issue and clarify it relates to forecasting. 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 how your data look
as clearly as possible.