Gas Price Prediction Service | OPIS Fuelstradamus

Discover gas price moves 24 hours in advance in local markets.

Find out if pump prices will increase, stay steady or decrease 24 hours in advance with OPIS Fuelstradamus. By applying data science and machine learning, OPIS creates an accurate predictive algorithm for price moves.

Who can benefit from this service?

FLEETS: Time fill-ups when gas prices change to minimize fuel expenses.
AUTOMAKERS/OEMs: Incorporate into connected car technology as a cost-saving tool for drivers. Increase brand loyalty. Drive sales on the showroom floor.
TRAVEL WEBSITES/APPs: Provide predictions to drivers to increase value of your service.

Fuelstradamus is delivered through an API for U.S. gasoline prices only.

How OPIS Derives Price Predictions

By providing transparency and reporting wholesale prices out of refineries and liquid terminals, OPIS is uniquely positioned to use advanced data science and machine learning techniques to evaluate market behavior over time. This intelligence determines how retail prices react to changes in costs on a regional level. With an average financial accuracy* over time approaching 90%, customers can rely on the predictions to find real cost savings for drivers.

*Financial accuracy is defined as a prediction that results in a positive or neutral impact on the cost of fuel being purchased.

Key Features and Benefits

Fleet companies can help drivers determine the best time to fill up company vehicles, adding significant savings to the organization’s bottom line.
Auto manufacturers can add this intelligence into connected car technology helping drivers save on fuel purchases. Consumer sentiment around fuel spending is negative, so positioning your car brand as a cost-savings tool to consumers will drive brand loyalty and help dealerships sell vehicles on the show room floor.

Fuel predictions are made at a county level 4x/day, updating based on the latest available data.
An API provides data output in two ways: (1) Return the county for the user based on their location information (latitude/longitude) and (2) Calling our predictions for all counties at once and storing it internally 4x/day and in turn pushing the predictions to users via existing data dissemination channels.