  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.
