Probabilistic Data Studio

What is this?

Probabilistic Data Studio turns any point forecast into a probabilistic forecast — showing a range of possible outcomes and their likelihood rather than a single predicted value. It fits a Gaussian process model to your data and displays fan-chart confidence bands.

How to use

• Example data — select a built-in dataset from the dropdown to load sample forecasts (sine waves, ERCOT load, solar, wind, LMPs).
• Paste your own data — type or paste up to 30 rows into the data grid. Use multiple columns for separate observations at each time step.
• Create forecast — click the button to fit a Gaussian process model. The chart shows the mean prediction with gold confidence bands.
• Adjust — after fitting, use the Shape, Uncertainty, Structure, and Distribution controls to fine-tune the model without re-running the full search.
• Quantile table — after fitting, a table of quantile values appears below the chart.

Key concepts

Gaussian Process

A non-parametric model that defines a distribution over functions. Given observed data, it produces a posterior mean and variance at every point — yielding natural confidence bands.

Kernel search

Nimbus automatically searches over kernel compositions (RBF, periodic, white noise) to find the best structure for your data, inspired by the Automatic Statistician.

Fan chart

The gold shaded bands show prediction intervals at increasing confidence levels, from the median outward.

References

1. Lloyd, J.R., Duvenaud, D., Grosse, R., Tenenbaum, J.B., & Ghahramani, Z. (2014). Automatic Construction and Natural-Language Description of Nonparametric Regression Models. arXiv:1402.4304. arxiv.org/abs/1402.4304

Disclaimer & Terms of Use

This tool is provided free of charge by Distill Energy for informational and educational purposes. You are free to use and reproduce this tool and its outputs. However, Distill Energy provides this tool "as is" without warranty of any kind, express or implied, and disclaims all liability for any damages, losses, or decisions arising from its use or its results. The probabilistic forecasts generated are statistical estimates and should not be relied upon as the sole basis for financial, operational, or investment decisions.