MOMENT 1 base EU AI Act Compliance Profile

MOMENT-base

MOMENT is a family of foundation models for general-purpose time-series analysis. The models in this family (1) serve as a building block for diverse time-series analysis tasks (e.g., forecasting, classification, anomaly detection, and imputation, etc.), (2) are effective out-of-the-box, i.e., with no (or few) task-specific exemplars (enabling e.g., zero-shot forecasting, few-shot classification, etc.), and (3) are tunable using in-distribution and task-specific data to improve performance.

For details on MOMENT models, training data, and experimental results, please refer to the paper MOMENT: A Family of Open Time-series Foundation Models.

MOMENT-1 comes in 3 sizes: Small, Base, and Large.

Usage

Recommended Python Version: Python 3.11 (support for additional versions is expected soon).

You can install the momentfm package using pip:

pip install momentfm

Alternatively, to install the latest version directly from the GitHub repository:

pip install git+https://github.com/moment-timeseries-foundation-model/moment.git

To load the pre-trained model for one of the tasks, use one of the following code snippets:

Forecasting

from momentfm import MOMENTPipeline

model = MOMENTPipeline.from_pretrained(
    "AutonLab/MOMENT-1-base", 
    model_kwargs={
        'task_name': 'forecasting',
        'forecast_horizon': 96
    },
)
model.init()

Classification

from momentfm import MOMENTPipeline

model = MOMENTPipeline.from_pretrained(
    "AutonLab/MOMENT-1-base", 
    model_kwargs={
        'task_name': 'classification',
        'n_channels': 1,
        'num_class': 2
    },
)
model.init()

Anomaly Detection, Imputation, and Pre-training

from momentfm import MOMENTPipeline

mode

Your obligations as a MOMENT 1 base deployer

Track your progress

Sign up to track obligations, receive deadline reminders, and generate audit packages.

Tags

Library