Toto-Open-Base-1.0

Toto-Open-Base-1.0

Toto (Time Series Optimized Transformer for Observability) is a state-of-the-art time-series foundation model designed for multi-variate time series forecasting, emphasizing observability metrics. Toto efficiently handles high-dimensional, sparse, and non-stationary data commonly encountered in observability scenarios.

<div style="width: 100%; margin: auto; padding: 1rem;"> <img src="figures/rankings.png" alt="model ranking" style="width: 100%; height: auto;" /> <em style="display: block; margin-top: 0.5rem; text-align: center;"> The average rank of Toto compared to the runner-up models on both the <a href="https://huggingface.co/spaces/Salesforce/GIFT-Eval">GIFT-Eval</a> and <a href="https://huggingface.co/datasets/Datadog/BOOM">BOOM</a> benchmarks (as of May 19, 2025). </em> </div>

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✨ Key Features

• Zero-Shot Forecasting: Perform forecasting without fine-tuning on your specific time series.
• High-Dimension Multi-Variate Support: Efficiently process multiple variables using Proportional Factorized Space-Time Attention.
• Decoder-Only Transformer Architecture: Support for variable prediction horizons and context lengths.
• Probabilistic Predictions: Generate both point forecasts and uncertainty estimates using a Student-T mixture model.
• Extensive Pretraining on Large-Scale Data: Trained on over 2 trillion time series data points, the largest pretraining dataset for any open-weights time series foundation model to date.
• Tailored for Observability Metrics with State-of-the-Art Performance on GIFT-Eval and BOOM.

<div style="width: 100%; margin: auto; padding: 1rem;"> <img src="figures/architecture.png" alt="model architecture" style="width: 100%; height: auto;" /> <em style="display: block; margin-top: 0.5rem; text-align: center;"> Overview of Toto-Open-Base-1.0 architecture. </em> </div>

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📚 Training Data Summary

• Observability Metrics: ~1 trillion points from Datadog internal systems (no customer data)
• Public Datasets:
  • GIFT-Eval Pretrain
  • Chronos datasets
• Synthetic Data: ~1/3 of training data

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⚡ Quick Start: Model Inference

Inference code is available on GitHub.

Installation

pip install toto-ts

For optimal speed and reduced memory usage, you should also install xFormers and flash-attention

🚀 Inference Example

Here's how to quickly generate forecasts using Toto:

⚠️ In our study, we take the median across 256 samples to produce a point forecast. This tutorial previously used the mean but has now been updated.

import torch
from toto.data.util.dataset import MaskedTimeseries
from toto.inference.forecaster import TotoForecaster
from toto.model.toto import Toto

DEVICE = 'cuda'

# Load pre-trained Toto model
toto = Toto.from_pretrained('Datadog/Toto-Open-Base-1.0').to(DEVICE)

# Optional: compile model for enhanced speed
toto.compile()

forecaster = TotoForecaster(toto.model)

# Example input series (7 variables, 4096 timesteps)
input_series = torch.randn(7, 4096).to(DEVICE)
timestamp_seconds = torch.zeros(7, 4096).to(DEVICE)
time_interval_seconds = torch.full((7,), 60*15).to(DEVICE)

inputs = MaskedTimeseries(
    series=input_series,
    padding_mask=torch.full_like(input_series, True, dtype=torch.bool),
    id_mask=torch.zeros_like(input_series),
    timestamp_seconds=timestamp_seconds,
    time_interval_seconds=time_interval_seconds,
)

# Generate forecasts for next 336 timesteps
forecast = forecaster.forecast(
    inputs,
    prediction_length=336,
    num_samples=256,
    samples_per_batch=256,
)

# Access results
median_prediction = forecast.median
prediction_samples = forecast.samples
lower_quantile = forecast.quantile(0.1)
upper_quantile = forecast.quantile(0.9)

For detailed inference instructions, refer to the inference tutorial notebook.

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💾 Available Checkpoints

🔗 Additional Resources

• Research Paper
• GitHub Repository
• Blog Post
• BOOM Dataset

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📖 Citation

If you use Toto in your research or applications, please cite us using the following:

@inproceedings{
cohen2026this,
title={This Time is Different: An Observability Perspective on Time Series Foundation Models},
author={Ben Cohen and Emaad Khwaja and Youssef Doubli and Salahidine Lemaachi and Chris Lettieri and Charles Masson and Hugo Miccinilli and Elise Ram{\'e} and Qiqi Ren and Afshin Rostamizadeh and Jean Ogier du Terrail and Anna-Monica Toon and Kan Wang and Stephan Xie and Zongzhe Xu and Viktoriya Zhukova and David Asker and Ameet Talwalkar and Othmane Abou-Amal},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year={2026},
url={https://openreview.net/forum?id=1jDAYXfcS2}
}