Revision Metrics
================

Revision metrics assess how index values change as new data becomes available. We will demonstrate how to calculate and visualize revision metrics.

Basic Setup
-----------

First, we will import the necessary modules and load some sales data and create a series of indices:

.. code-block:: python

    >>> from hpipy.datasets import load_seattle_sales
    >>> from hpipy.price_index import RepeatTransactionIndex

    >>> df = load_seattle_sales()

    >>> hpi = RepeatTransactionIndex.create_index(
    ...     trans_data=df,
    ...     prop_id="pinx",
    ...     trans_id="sale_id",
    ...     price="sale_price",
    ...     date="sale_date",
    ...     periodicity="M",
    ...     estimator="robust",
    ...     log_dep=True,
    ...     smooth=True,
    ... )

    >>> hpi_series = hpi.create_series(train_period=24, max_period=30)

Calculating Revision
--------------------

Calculate the revision of the index using the ``revision`` function:

.. code-block:: python

    >>> from hpipy.utils.metrics import revision

    >>> rev = revision(hpi_series)
    >>> rev.round(5).head()
       period     mean   median
    0       1  0.00000  0.00000
    1       2 -0.16127 -0.24276
    2       3 -1.10777  0.03733
    3       4 -2.15903 -1.22027
    4       5 -1.57049 -1.09691