MOIRAI: Salesforce’s Foundation Model for Time-Series Forecasting | by Nikos Kafritsas | Mar, 2024

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Code, model weights, and data will be released soon

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Time series foundation models are finally taking off!

The previous articles explored 2 promising foundation forecasting models, TimeGPT and TimesFM.

This article will explore MOIRAI [1], a groundbreaking TS foundation model by Salesforce. MOIRAI is awesome in terms of performance — but more importantly, the authors have pledged to open-source the model and its training dataset!

This is mentioned in a tweet here by Caiming Xiong, VP of AI at Salesforce and one of the paper’s authors

The major contributions of this paper are the following:

  • MOIRAI: A novel transformer-encoder architecture, functioning as a universal time-series forecasting model.
  • LOTSA (Large Open Time Series Archive): The largest collection of open time series datasets with 27B observations across 9 domains.
  • UNITS: An open-source library for training universal time-series models.

Moreover, this article discusses:

  1. How MOIRAI works and why it’s a powerful model.
  2. How MOIRAI performs compared to Google’s TimesFM
  3. MOIRAI benchmark results.
  4. Why MOIRAI will revolutionize the TS forecasting field.

Let’s get started.

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We described the challenges in detail here. To recap, these are:

  • Difficulty finding public time-series data — for training a time-series foundation model.
  • Time-series data are highly heterogeneous — unlike in NLP, where data have well-defined grammar and vocabulary.
  • Time series can be multivariate — unlike in NLP, where input is one-dimensional.
  • Time series have different granularities — daily, weekly, monthly, etc.