MOMENT Large 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: Alternatively, to install the latest version directly from the GitHub repository: To load the pre trained model for one of the tasks, use one of the following code snippets: Forecasting Classification Anomaly Detection, Imputation, and Pre training Representation Learning Tutorials Here is the list of tutorials and reproducibile experimen…
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