README.md

metadata

license: other
license_name: ci-proto-licence
license_link: https://huggingface.co/ContinuousIntelligence/ci-proto/blob/main/LICENCE
library_name: continuousintelligence
pipeline_tag: time-series-forecasting
tags:
  - time-series
  - forecasting
  - probabilistic-forecasting
  - foundation-model

Introducing ci-proto, our frontier model that says goodbye to context windows and hello to accuracy

State-of-the-art forecasting at your fingertips.

• 2M+ context window. Process 64× the history of any other time-series foundation model.
• Frontier accuracy. Leading forecasting performance on fev-bench.
• CPU and GPU support. Run locally through the same Python API.
• Multivariate forecasting. Turn relationships between signals into more accurate forecasts.
• Uncertainty included. Get nine quantiles between 0.1 and 0.9 to plan for a range of outcomes.

Make your first forecast · See the results · Join the waitlist for our next release · Join our Design Partner Program

Make your first forecast

Your first forecast in a few lines. Install with Python 3.10–3.14:

pip install continuousintelligence

Forecast the next day of bicycle rentals from five observed days of hourly demand in Washington, D.C. The example history is included with the package:

import continuousintelligence as ci

# Load the model (uses CUDA when available).
model = ci.load_model("ContinuousIntelligence/ci-proto")

# Load example history as a torch.Tensor shaped [variables, time].
history = ci.load_example()

# Forecast the next 24 hourly time steps.
prediction = model.forecast(history, horizon=24)

# Plot the forecast and its uncertainty bands.
ci.plot_forecast(history, prediction, model.quantiles, output="forecast.png")

The next 24 hours, with uncertainty included. The shaded region marks the forecast day. The curve shows the model's median forecast, surrounded by nested quantile bands. Replace the example history with your own to get started.

Data: Hadi Fanaee-T (2013), Bike Sharing, UCI, licensed under CC BY 4.0.

Dive deeper. The walkthrough notebook extends this example with calendar covariates, practical uses of the quantiles, and forecasting through missing data.

Frontier performance

The #1 foundation model on fev-bench.

Talk to the team

Tell us what you need to predict. We want to hear about the decisions you're trying to improve, the data you're working with, and where current methods fall short.

Whether you're exploring a new idea or improving an existing system, we'd like to work with you on a solution. Share your use case, your constraints, and what success would look like, and let's discuss how we can help.

Join our Design Partner Program

Beyond ci-proto

Frontier performance is our starting point. ci-proto gives you a first look at our approach to time-series intelligence. Our next release builds on that foundation. Join the waitlist to be the first to hear what's new, see the results, and find out when you can try it.

Join the waitlist

Usage details

Covariates, categorical inputs, timestamps, related series, revision pinning, and CPU and GPU setup are documented in the package README.

Model details

• Developed by: Continuous Intelligence Limited
• Model type: pretrained time-series foundation model for zero-shot probabilistic forecasting
• Parameters: 167M
• Inputs: one or more target series, with optional past and future covariates, timestamps and related series
• Outputs: nine quantiles from 0.1 to 0.9 for every target and forecast step
• Files: model.ckpt (histories up to 16,384 steps), model-long.ckpt (longer histories, selected automatically), tta.json (preprocessing and calibration settings) and config.json (model information)

Training and evaluation

• Training data: a large corpus of real-world and synthetic time series.
• Evaluation: zero-shot on all 100 tasks of fev-bench, with the released preprocessing and calibration in tta.json, scored by scaled quantile loss.

Licence

The model weights are released under the ci-proto Code and Model Licence.

Citation

@misc{ci-proto-2026,
  title  = {ci-proto},
  author = {{Continuous Intelligence Limited}},
  year   = {2026},
  url    = {https://huggingface.co/ContinuousIntelligence/ci-proto}
}