MOMENT: A Family of Open Time-series Foundation Models (AI summary)

Key Points

1. The paper introduces MOMENT, a family of open-source foundation models for general-purpose time-series analysis. It addresses the challenges of compiling a large and diverse collection of public time-series data, multi-dataset pre-training, and experimental benchmarks for time-series foundation models.

2. MOMENT is a family of high-capacity transformer models pre-trained using a masked time-series prediction task on large amounts of time-series data drawn from diverse domains. It can be effectively used for diverse time-series analysis tasks and is tunable using in-distribution and task-specific data to improve performance.

3. The paper presents significant empirical observations about large pre-trained time-series models and highlights the challenges faced in time-series modeling, such as diversity in time-series characteristics and limited supervision settings.

4. The authors introduce the Time-series Pile, a large collection of public time-series databases from diverse domains used for pre-training and evaluation. They carefully split each dataset into disjoint training, validation, and test splits to minimize data contamination.

5. The models in the MOMENT family are evaluated using a benchmark to assess performance on diverse tasks like short- and long-horizon forecasting, classification, anomaly detection, and imputation under limited supervision settings.

6. Experiments demonstrate that MOMENT is effective for multiple time-series analysis tasks in limited supervision settings. It outperforms several state-of-the-art deep learning and statistical machine learning models across various tasks.

7. The paper explores the interpretability of MOMENT and demonstrates that it can capture intuitive time-series characteristics such as trend, amplitude, frequencies, and phases of time-series.

8. Findings show that model scaling improves training loss, pre-trained models on time-series can solve sequence classification tasks, and randomly initialized MOMENT leads to lower training loss.

9. The authors provide an exhaustive list of hyperparameters, open-source the code, and evaluate the transparency and environmental impact of MOMENT. They emphasize the importance of using the model's predictions with care in high-stakes settings.

Summary

The paper introduces MOMENT, a family of open-source foundation models for general-purpose time-series analysis. It identifies the challenges in pre-training large models on time-series data, which include the absence of a cohesive public time-series repository, the diverse characteristics of time-series data that make multi-dataset training difficult, and the nascent stages of experimental benchmarks to evaluate these models. To address these challenges, the authors 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. They also design a benchmark to evaluate time-series foundation models on diverse tasks and datasets in limited supervision settings. The paper presents several interesting empirical observations about large pre-trained time-series models, including insights about model performance on various time-series analysis tasks, such as forecasting, classification, anomaly detection, and imputation.

Additionally, it explores the effectiveness of pre-trained models with minimal data and task-specific fine-tuning. MOMENT is designed to serve as a building block for diverse time-series analysis tasks, offer effective out-of-the-box performance, and be tunable using in-distribution and task-specific data to improve performance. Furthermore, MOMENT is a family of high-capacity transformer models pre-trained using a masked time-series prediction task on large amounts of time-series data drawn from diverse domains. The paper discusses MOMENT's key contributions related to pre-training data, multi-dataset pre-training, and evaluation, and outlines its use in various time-series analysis tasks.

The authors also propose speculative research directions for the future, such as applying MOMENT to real-world challenges and investigating multi-modal time-series and text foundation models. Lastly, the paper holds discussions on transparency, environmental impact, ethical considerations, and potential misuse of the model.

Reference: https://arxiv.org/abs/2402.03885