Can freight rates really be predicted?

Not perfectly — but the lagged relationships between trade flows, congestion, vessel supply and indices are statistically learnable, which lets a model give a probabilistic forward curve that beats gut feel for fixture timing.

What data do I need to forecast freight rates?

Commodity trade flows, vessel supply and positioning, freight indices such as the BDI, and bunker prices are the core inputs. NAVGreen fuses these automatically into one forward curve.

How to Forecast Dry Bulk Freight Rates with AI

Freight rates for dry bulk carriers can swing by double digits within a week. For owners and operators, fixing a vessel a few days early — or late — is often the difference between a profitable voyage and a loss-making one. AI-driven forecasting turns that timing from guesswork into a measurable edge.

The data that actually moves freight rates

Commodity trade flows — iron ore, coal and grain shipments that drive tonne-mile demand.
Vessel supply — fleet availability, ballast positioning and port congestion.
Freight indices — the Baltic Dry Index (BDI) and its sub-indices as leading signals.
Bunker prices — fuel cost as a direct input to voyage economics.

Why time-series models beat gut feel

A well-trained time-series model learns the lagged relationships between these signals and forward rates — for example, how a build-up of congestion at loading ports tends to tighten tonnage and lift rates two to three weeks later. NAVGreen fuses these signals into a single forward curve so a fixture decision can be stress-tested against the predicted market, not last week’s.

99.8%prediction accuracy of NAVGreen’s fused forecasting core (NOAA + ECMWF weather + market signals).

From forecast to fixture

A forecast only pays off when it changes a decision. The practical workflow: pull the forward rate curve, overlay your candidate fixtures, and lock in when the predicted downside outweighs the upside of waiting. That is exactly what the freight prediction module is built to do.