Salesforce AI Research has unveiled Moirai 2.0, the latest advancement in the world of time series foundation models. Built atop a decoder-only transformer architecture, Moirai 2.0 sets a new bar for performance and efficiency, claiming the #1 spot on the GIFT-Eval benchmark-the gold standard for time-series forecasting model evaluation. Not only is it 44% faster in inference and 96% smaller in size compared to its predecessor, but this substantial leap comes without sacrificing accuracy—making it a game-changer for both research and enterprise environments.
What Makes Moirai 2.0 Special?
Architecture Innovations
- Decoder-only Transformer: The switch from a masked encoder to a decoder-only transformer empowers Moirai 2.0 to better model autoregressive forecast generation, enhancing scalability and performance on larger, more complex datasets.
- Efficient Multi-Token Prediction: By predicting multiple tokens at a time (rather than just one), the model achieves greater efficiency and stability during forecasting.
- Advanced Data Filtering: Low-quality, non-forecastable time series are automatically filtered out during training, improving robustness.
- Patch Token Embedding & Random Masking: New techniques in encoding missing value information and robustness to incomplete data during inference.
Expanded Dataset for Pretraining
Moirai 2.0 leverages a richer mix of training data:
- Real-world sets like GIFT-Eval Pretrain and Train
- Chronos mixup: Synthetic time series blending for diversity
- KernelSynth procedures from Chronos research
- Internal operational data from Salesforce IT systems
This broad data foundation enables Moirai 2.0 to generalize across countless forecasting tasks and domains.
Performance: Breaking New Ground
Moirai 2.0 is a leap beyond its predecessors:
- Best MASE Score on GIFT-Eval for non-data-leaking models (industry-accepted metric for forecast accuracy)
- CRPS Performance matches previous state-of-the-art
- Compared to Moirai_large:
- 16% better on MASE
- 13% better on CRPS
- 44% faster in inference
- 96% smaller parameter size
These results make high-performance, scalable forecasting more accessible to a broader audience.
Why Moirai 2.0 Matters for Practitioners
Moirai 2.0’s capabilities extend beyond academic benchmarks into enterprise-critical domains such as:
- IT Operations: Proactive capacity scaling, anomaly detection
- Sales Forecasting: Accurate, scalable revenue predictions
- Demand Forecasting: Optimized inventory management
- Supply Chain Planning: Better scheduling, reduced waste
- And many more data-driven business processes
With dramatically reduced model size and improved speed, high-quality forecasting can now be applied at scale—empowering businesses to make smarter, faster decisions regardless of their data infrastructure.
Getting Started: Moirai 2.0 in Practice
Integration is seamless for developers and data scientists. Here’s a typical workflow, leveraging open-source modules available on Hugging Face:
Sample Python Workflow
Import Libraries
import matplotlib.pyplot as plt
from gluonts.dataset.repository import dataset_recipes
from uni2ts.eval_util.data import get_gluonts_test_dataset
from uni2ts.model.moirai2 import Moirai2Forecast, Moirai2Module
Load Moirai 2.0
model = Moirai2Forecast(
module=Moirai2Module.from_pretrained("Salesforce/moirai-2.0-R-small"),
prediction_length=100,
context_length=1680,
target_dim=1,
feat_dynamic_real_dim=0,
past_feat_dynamic_real_dim=0
)
Load Dataset & Generate Forecasts
test_data, metadata = get_gluonts_test_dataset("electricity", prediction_length=None, regenerate=False)
predictor = model.create_predictor(batch_size=32)
forecasts = predictor.predict(test_data.input)
Visualize Results
# Example visualization
fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(25, 10))
# Use Moirai plotting utility to display forecasts
Full examples and notebook links are provided by Salesforce for deeper experimentation.
Universal, Scalable, Robust
By democratizing access to cutting-edge, general-purpose forecasting technology, Moirai 2.0 is poised to reshape the landscape of time series modeling. With flexibility across domains, better robustness, faster inference, and lower computational demands, Salesforce AI Research’s model paves the way for businesses and researchers globally to harness the power of forecasting for transformative decision making.
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