Comparability and Normalization

What is normalization in a price assessment?

Normalization is the adjustment of gathered market information so it matches the base standard reflected in the price assessments. Markets are diverse: trades differ in delivery location, data quality, quantity, specification, and timing — merely averaging them would not represent the assessment value accurately. Normalization occurs concomitantly and iteratively with modeling, not as a strictly sequential step before it.

Each kind of difference has its own correction:

Difference across sources	How it is corrected
Delivery location	Freight rates
Data quality	Factors, indexes, and cross-reference with other sources
Quantity and specification	Averages and outlier detection
Timing between assessment curves	Mathematical formulas (interpolation)

How are prices adjusted for location differences?

Trades reflecting different delivery locations are adjusted using freight rates, so every figure is expressed at the published location basis — typically the location where trading of that commodity is most common. This is how, for example, Energy Prices & Markets publishes energy commodity prices on a per-country basis: the same underlying market information, translated to each country's reference location through freight adjustments.

How are currencies and units handled?

The conversion runs both ways. Internally, every monetary figure is standardized to US dollars using a single set of reference exchange rates, and every physical quantity to the International System of Units (SI) using fixed conversion factors — the common basis on which all comparisons and models operate (see the Currency and Units pages for the underlying references). At publication, figures are then converted from this default basis to the currencies and units commonly used in each country, so readers see values in their familiar local conventions.

How are slower series aligned to the monthly cadence?

Some official series are published quarterly or annually — less often than the monthly cadence assessments follow. These slower series are aligned to the monthly grid through interpolation — the mathematical-formula correction the normalization framework applies to timing differences between assessment curves. Filling the months between two official data points this way keeps every series, however it was originally reported, on the same monthly timeline as the rest of the dataset. That shared timeline is what makes cross-series and cross-country comparison valid within a single edition: because every input is aligned to the same monthly cutoff, no series is more or less current than another, and any given month can be compared consistently across the full dataset.

How are published figures rounded?

At the end of the transformation stage, every figure is rounded according to its data-status label: historical and Preliminary values round to three significant figures, Forecast values to two. The gap is deliberate honesty about precision — a Forecast predicts a period not yet reported, so it carries more uncertainty than a Preliminary estimate or a verified Final value, and the coarser rounding signals that lower certainty at a glance rather than implying false precision. Once official data arrives and a figure moves from Preliminary to Final, the three-significant-figure rounding already in place carries over unchanged; only the Forecast-to-Preliminary transition changes the rounding itself.