MOMENT: A Family of Open Time-series Foundation Models (2402.03885v3)

Published 6 Feb 2024 in cs.LG and cs.AI

Abstract: We introduce MOMENT, a family of open-source foundation models for general-purpose time series analysis. Pre-training large models on time series data is challenging due to (1) the absence of a large and cohesive public time series repository, and (2) diverse time series characteristics which make multi-dataset training onerous. Additionally, (3) experimental benchmarks to evaluate these models, especially in scenarios with limited resources, time, and supervision, are still in their nascent stages. To address these challenges, we compile a large and diverse collection of public time series, called the Time series Pile, and systematically tackle time series-specific challenges to unlock large-scale multi-dataset pre-training. Finally, we build on recent work to design a benchmark to evaluate time series foundation models on diverse tasks and datasets in limited supervision settings. Experiments on this benchmark demonstrate the effectiveness of our pre-trained models with minimal data and task-specific fine-tuning. Finally, we present several interesting empirical observations about large pre-trained time series models. Pre-trained models (AutonLab/MOMENT-1-large) and Time Series Pile (AutonLab/Timeseries-PILE) are available on Huggingface.

Authors (6)

Mononito Goswami (17 papers)
Konrad Szafer (1 paper)
Arjun Choudhry (14 papers)
Yifu Cai (23 papers)
Shuo Li (179 papers)
Artur Dubrawski (67 papers)

Citations (60)

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Summary

Unveiling MOMENT: The Open Time-Series Foundation Model Suite

Introduction to MOMENT

Time-series analysis, encompassing a broad spectrum of applications from weather forecasting to anomaly detection in network traffic or health monitoring, remains one of the most critical and challenging areas of data science. Despite its significance, the field has long grappled with the absence of large, cohesive, publicly available time-series datasets. This has, in turn, hindered the development of large-scale, pre-trained models that could potentially revolutionize the way we approach time-series analysis, akin to the transformations witnessed in NLP and computer vision (CV) through the advent of transformer models.

Addressing this gap, the collaborative efforts led by the team from Carnegie Mellon University and the University of Pennsylvania bring forth MOMENT—a family of open-source, large pre-trained time-series models. This new development not only paves the way for significant advancements in time-series analysis but also opens up new avenues for research and application in the domain.

Core Contributions of MOMENT

MOMENT stands out with its novel approach to addressing the foundational challenges in time-series analysis through:

Pre-training Data Compilation: Dubbed "The Time-series Pile", this extensive collection spans various domains, including healthcare, engineering, and finance, overcoming the limitation of data scarcity for pre-training purposes.
Multi-Dataset Pre-training: By tailoring the transformer architecture for time-series data, MOMENT efficiently handles the inherent diversity and challenges of time-series datasets, facilitating effective multi-dataset pre-training.
Benchmarking for Limited Supervision: The team goes a step further by designing a comprehensive benchmarking framework that evaluates the models' performance across a range of tasks with limited data availability, fine-tuning, and supervision. This move is particularly significant as it aligns closely with real-world scenarios where data or computational resources may be sparse.

Practical Implications and Theoretical Insights

From a practical standpoint, MOMENT demonstrates impressive versatility and efficacy across several time-series analysis tasks, including forecasting, classification, anomaly detection, and imputation. Remarkably, the models achieve state-of-the-art or near state-of-the-art performance, outshining both traditional statistical models and other deep learning approaches in settings with limited supervision.

Theoretically, MOMENT advances our understanding of time-series analysis in several ways. It showcases the feasibility and benefits of large-scale, multi-dataset pre-training for time-Series data—a relatively unexplored territory until now. Moreover, the models' ability to encode implicit time-series features, like trend and frequency, opens up new research directions in time-series representation learning.

Future Directions and Ethical Considerations

The introduction of MOMENT not only sets new benchmarks in time-series analysis but also establishes a strong foundation for future innovations. Its open-source nature encourages further development and fine-tuning by the broader research community. Subsequent studies could explore the integration of MOMENT with multimodal data or extend its pre-training framework to accommodate causal models for forecasting.

In line with ethical AI development, the MOMENT project emphasizes transparency, releasing extensive documentation and methodological details. Future work should continue to address potential biases, data privacy, and the environmental impact of training large models, ensuring that advancements in time-series analysis are both socially responsible and sustainable.

Conclusion

MOMENT represents a significant leap forward in time-series analysis, propelling the field towards more sophisticated, efficient, and generalizable methods. By tackling the longstanding challenges of data diversity and scarcity head-on, MOMENT not only enriches the toolbox of data scientists but also broadens the horizons for novel applications and theoretical exploration in time-series analysis.