Data
====

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   **Try the API v2 pre-release!**

   | You are viewing Documentation of v1 Data Pipeline. A New API version 2 is in development.
   | Try it out before release: :doc:`v2 Data Pipeline <data_v2>` | :doc:`v2 API Reference <api_v2>`
   | **Caution: v2 is WIP and unstable. Not yet production-ready.**

.. currentmodule:: pytorch_forecasting.data

Loading data for timeseries forecasting is not trivial - in particular if covariates are included and values are missing.
PyTorch Forecasting provides the :py:class:`~timeseries.TimeSeriesDataSet` which comes with a :py:meth:`~timeseries.TimeSeriesDataSet.to_dataloader`
method to convert it to a dataloader and a :py:meth:`~timeseries.TimeSeriesDataSet.from_dataset` method to create, e.g. a validation
or test dataset from a training dataset using the same label encoders and data normalization.

Further, timeseries have to be (almost always) normalized for a neural network to learn efficiently. PyTorch Forecasting
provides multiple such target normalizers (some of which can also be used for normalizing covariates).


Time series data set
---------------------

The time series dataset is the central data-holding object in PyTorch Forecasting. It primarily takes
a pandas DataFrame along with some metadata. See the :ref:`tutorial on passing data to models <passing-data>` to learn more it is coupled to models.

.. autoclass:: pytorch_forecasting.data.timeseries.TimeSeriesDataSet
   :noindex:
   :members: __init__

Details
--------

See the API documentation for further details on available data encoders and the :py:class:`~timeseries.TimeSeriesDataSet`:

.. currentmodule:: pytorch_forecasting

.. autosummary::
   :toctree: api

    data.encoders.EncoderNormalizer
    data.encoders.GroupNormalizer
    data.encoders.MultiNormalizer
    data.encoders.NaNLabelEncoder
    data.encoders.TorchNormalizer
    data.samplers.TimeSynchronizedBatchSampler
    data.samplers.GroupedSampler
    data.timeseries.TimeSeriesDataSet