What happens when your forecast is wrong?

At Helios, our forecasting is built on rigorous data science, market expertise, and climate analytics. That said, no forecast is perfect. When we find a prediction that doesn’t match real-world outcomes, we treat it as a valuable signal to improve our models.

We immediately dig into the drivers of the error:

-Was there an unforeseen market event (e.g., sudden export bans, political disruptions, terrorist attack, war)?

-Did new climate anomalies emerge that weren’t part of the historical record?

-Was there a data quality or reporting issue from one of our external sources?

This helps us pinpoint whether the miss came from data inputs, modeling assumptions, or unexpected external shocks.

Once we understand the cause, our data science team updates the relevant components:

-Retraining models with the new data so similar events are captured in future forecasts.

-Adjusting weighting in our WA%R (weighted average percent of risk) and seasonal risk models if climate signals didn’t trigger as expected.

-Incorporating new market or climate variables if they prove to have predictive power.

Any model changes are stress-tested against historical data to ensure improvements actually enhance predictive accuracy—and don’t degrade performance in other markets or timeframes.

If the miss is material, we communicate it in our reports and platform updates, providing context on:

-What happened

-Why it wasn’t captured in the forecast

-How we’ve adjusted to account for it going forward

This transparency builds trust and keeps clients informed.

We treat every miss as a data point that strengthens our forecasting framework. The more unexpected events we capture, the more resilient and adaptable our predictions become.