Get ready to brace yourself! Commodity prices are surging

Accuracy is the biggest casualty in price forecasting

There is skepticism about the validity of commodity price predictions premised solely on past events and to the exclusion of other variables.

A multivariate model can forecast outcomes better

Businesses hunger for best-in-class accuracy in their price forecasts with a range of possible variables factored in. Our forecasting framework is predicated on a multivariable model, besides historical trends of individual commodities and curated database. As a result, our clients benefit from a continuous stream of precise competitive market insights.

In the medium-to-long-term, prices tend to defy predictions

Many commodity trackers get their short-term forecasts right, but predictions for longer timeframes are almost always off the mark, miffing users.

The short answer is “Maximize your data on price-drivers”

Making accurate price estimates for the longer term is entirely feasible so long as the forecasting engine is using broad-based data, covering the swathe of factors that drive commodity prices. Our COMET platform draws on price data for over “250 price series” histories and forecasts, providing maximum insight into price-driving factors.

Outlier events often trump traditional predictive models

The biggest inadequacy of conventional commodity price forecasting models is that they are not necessarily prepared for black swan events.

There is a need to continually reset the forecasting configuration

While no model is omniscient, it is crucial that forecasting algorithms are retrained on fresh datasets and events (weather, tariffs) to improve forecast accuracy. The forecasting algorithms we use are dependent not just on macroeconomic factors but also on broader parameters to prepare traders for random and unexpected events (“black swans”).

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