Global Predictions outperforms major economic benchmarks like the IMF World Economic Outlooks

We combine a diversity of data sources into our World Simulation Engine to make accurate predictions and forecasts about trends, markets, and risks.

World Simulation Engine architecture diagram
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Focused on accuracy

Rather than trying to tell the most compelling narrative, we are focused on forecasting accuracy to a level rarely seen in the economic sector. There is a fundamental belief at Global Predictions that the only thing that truly matters is the ability to predict real world results accurately. All predictions are back-tested, and measured against real-world data, across 10,000s of indicator time series.

The world is complex, therefore the models need to be complex. Modeling real estate or GDP requires an understanding of macroeconomics, technology trends, public health, financial markets, and even politics. The Global Predictions model uses a large model of models approach to maintain a widespread understanding of what is happening in the macro economy at all times, predicted out 2-12 months into the future.

Model of models

AI/ML + economics

The Global Predictions model uses a mix of state-of-the-art Machine Learning techniques, statistical forecasting, and classic economic models combined to make both accurate, dependable, and generalized forecasts. This mix avoids overfitting and the breadth allows for prediction of non-linear extreme or fat tailed scenarios.

Transparency in accuracy

Accuracy results shown for transparency

Global Predictions outperforms major economic benchmarks like the IMF World Economic Outlook

Side by side comparison with the IMF World Economic Outlook showing Global Predictions outperforms in accuracy by a large margin

Note: lower error represents higher accuracy

The relative percentage error is calculated as the absolute difference divided by the baseline of the last year economic data was available. Benchmarks retrieved from: International Monetary Fund World Economic Outlook Database, April 2015 to October 2020.