  Timely monetary policy decision-making requires timely core inflation
measures. We create a new core inflation series that is explicitly designed to
succeed at that goal. Precisely, we introduce the Assemblage Regression, a
generalized nonnegative ridge regression problem that optimizes the price
index's subcomponent weights such that the aggregate is maximally predictive of
future headline inflation. Ordering subcomponents according to their rank in
each period switches the algorithm to be learning supervised trimmed inflation
- or, put differently, the maximally forward-looking summary statistic of the
realized price changes distribution. In an extensive out-of-sample forecasting
experiment for the US and the euro area, we find substantial improvements for
signaling medium-term inflation developments in both the pre- and post-Covid
years. Those coming from the supervised trimmed version are particularly
striking, and are attributable to a highly asymmetric trimming which contrasts
with conventional indicators. We also find that this metric was indicating
first upward pressures on inflation as early as mid-2020 and quickly captured
the turning point in 2022. We also consider extensions, like assembling
inflation from geographical regions, trimmed temporal aggregation, and building
core measures specialized for either upside or downside inflation risks.
