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Salesforce AI releases Moirai 2.0: Salesforce’s latest time series basic model is built on a decoder-only transformer architecture

by · August 15, 2025

Salesforce AI research has been revealed Moirai 2.0the latest advances in the basic model of time series. Built on A Transformer architecture for decoder onlyMoirai 2.0 sets a new performance and efficiency standard, claiming to be number one on the gift world benchmark – the gold standard for time series prediction model evaluation. Not only Reasoning 44% faster, 96% smaller This substantial leap compared to its predecessor is No sacrificing accuracy– Change it into the game rules of research and corporate environment.

What makes Moirai 2.0 unique?

Architectural Innovation

  • Decoder-only transformers: Switching from a mask encoder to a decoder-only transformer enables Moirai 2.0 to better model automatic regression prediction generation, enhancing scalability and performance on larger and more complex datasets.
  • Effective multi-talk prediction: By predicting multiple tokens at once (rather than one) the model achieves higher efficiency and stability in the prediction process.
  • Advanced data filtering: Low-quality intangible heritage time series will be automatically filtered out during training, thereby improving robustness.
  • Patch token embed and random masking: New technologies that encode missing value information and robustness during inference.

Extended datasets for preprocessing

Moirai 2.0 utilizes A A richer combination of training data:

  • Real world similar Gift review and train
  • Timed Mix: Comprehensive time series fusion diversity
  • kernelsynth Chronos Research Programs
  • Internal operation data of Salesforce IT system

This extensive data base enables Moirai 2.0 to span numerous prediction tasks and domains.

Performance: Break new ground

Moirai 2.0 is beyond its predecessors:

  • Best MASE score Gift about non-DATA penetration models (with industry-recognized indicators, prediction accuracy)
  • CRPS performance Match the previous state-of-the-art
  • Compared to moirai_large:
    • 16% on Mase
    • 13% on CRP
    • Reasoning 44% faster
    • 96% smaller parameter size

These results make high-performance, scalable predictions more accessible to a wider audience.

Why Moirai 2.0 is important to practitioners

Moirai 2.0 features beyond academic benchmarks until the critical areas of the enterprise For example:

  • IT Operations: Active capability scaling, abnormal detection
  • Sales Forecast: Accurate, scalable revenue forecast
  • Demand forecast: Optimized inventory management
  • Supply Chain Planning: Better arrangements and reduce waste
  • There are more data-driven business processes

With the dramatic reduction in model size and speed increase, high-quality predictions can now be applied at scale to give businesses smarter and faster decisions regardless of their data infrastructure.

Getting started: Moirai 2.0 in practice

Integration is seamless for developers and data scientists. This is a typical workflow that takes advantage of open source modules available on the embrace surface:

Sample Python workflow

Import library

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

Loading 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 the dataset and generate predictions

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

Salesforce provides complete examples and notebook links for more in-depth experiments.

Universal, scalable, robust

By enabling access to cutting-edge, universal prediction technologies, Moirai 2.0 is expected to reshape the landscape of time series modeling. Salesforce AI Research’s model paves the way for global enterprises and researchers to leverage prediction capabilities to make transformative decisions in a cross-domain flexibility, better robustness, faster inference, and lower computing needs.

Check Technical details and Hug the face (model). Check out ours anytime Tutorials, codes and notebooks for github pages. Also, please stay tuned for us twitter And don’t forget to join us 100K+ ml reddit And subscribe Our newsletter.


Asif Razzaq is CEO of Marktechpost Media Inc. As a visionary entrepreneur and engineer, ASIF is committed to harnessing the potential of artificial intelligence to achieve social benefits. His recent effort is to launch Marktechpost, an artificial intelligence media platform that has an in-depth coverage of machine learning and deep learning news that can sound both technically, both through technical voices and be understood by a wide audience. The platform has over 2 million views per month, demonstrating its popularity among its audience.

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