Moment

Abstract

1. Introduction

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 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 its 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.

MOMENT: A Family of Open Time-series Foundation Models

2. Objective

MOMENT: A Family of Open Time-series Foundation Models, ICML'24

This project demonstrates how TypeScript can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Node.js (LTS) and npm / yarn / pnpm
• VS Code or any code editor
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/moment-timeseries-foundation-model/moment.git
cd moment

pip install momentfm

pip install git+https://github.com/moment-timeseries-foundation-model/moment.git

from momentfm import MOMENTPipeline

model = MOMENTPipeline.from_pretrained(
    "AutonLab/MOMENT-1-large",
    model_kwargs={
        "task_name": "forecasting",
        "forecast_horizon": 96
    },
)
model.init()

from momentfm import MOMENTPipeline

model = MOMENTPipeline.from_pretrained(
    "AutonLab/MOMENT-1-large",
    model_kwargs={
        "task_name": "classification",
        "n_channels": 1,
        "num_class": 2
    },
)
model.init()

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by moment-timeseries-foundation-model and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.