MOMENT: A Family of Open Time-series Foundation Models
Mononito Goswami, Konrad Szafer, Arjun Choudhry, Yifu Cai, Shuo Li,, Artur Dubrawski

TL;DR
MOMENT introduces a family of open-source foundation models for time series analysis, leveraging a large, diverse dataset and a new benchmark to evaluate performance in limited supervision scenarios.
Contribution
The paper presents MOMENT, a novel approach to pre-training large-scale time series models using the comprehensive Time Series Pile and a new evaluation benchmark.
Findings
Models perform well with minimal data and fine-tuning.
Pre-training improves performance across diverse tasks.
Empirical observations reveal insights about large time series models.
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.…
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Taxonomy
TopicsTime Series Analysis and Forecasting · Simulation Techniques and Applications
