Authors: Mononito Goswami, Konrad Szafer, Arjun Choudhry, Yifu Cai, Shuo Li, Artur Dubrawski

Published on: February 06, 2024

Impact Score: 8.22

Arxiv code: Arxiv:2402.03885

Summary

• What is new: Introduction of MOMENT, a new family of foundational models specifically for time-series analysis, leveraging a unique compilation of diverse public time-series data.
• Why this is important: The challenges of pre-training large models on time-series data include the lack of a cohesive public time-series repository and the difficulty in handling diverse time-series characteristics, along with the absence of adequate benchmarks.
• What the research proposes: The development of the Time-series Pile, a large, diverse collection of public time-series data, and the creation of a new benchmark for evaluating foundation models in time-series analysis under limited supervision.
• Results: Pre-trained models using the Time-series Pile have shown effectiveness with minimal data and task-specific fine-tuning across diverse tasks, along with providing new empirical insights into large pre-trained time-series models.

Technical Details

Technological frameworks used: Foundation model framework for time-series analysis.

Models used: MOMENT models.

Data used: The Time-series Pile, a repository of diverse public time-series data.

Potential Impact

Financial services, healthcare, retail, and any sector reliant on time-series data for predictive analytics and decision-making could benefit or need to adapt to these insights.

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