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:

>>> 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:

>>> 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