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Code, mannequin weights, and knowledge can be launched quickly
Time collection basis fashions are lastly taking off!
The earlier articles explored 2 promising basis forecasting fashions, TimeGPT and TimesFM.
This text will discover MOIRAI [1], a groundbreaking TS basis mannequin by Salesforce. MOIRAI is superior when it comes to efficiency — however extra importantly, the authors have pledged to open-source the mannequin and its coaching dataset!
That is talked about in a tweet here by Caiming Xiong, VP of AI at Salesforce and one of many paper’s authors
The main contributions of this paper are the next:
- MOIRAI: A novel transformer-encoder structure, functioning as a common time-series forecasting mannequin.
- LOTSA (Giant Open Time Collection Archive): The biggest assortment of open time collection datasets with 27B observations throughout 9 domains.
- UNITS: An open-source library for coaching common time-series fashions.
Furthermore, this text discusses:
- How MOIRAI works and why it’s a robust mannequin.
- How MOIRAI performs in comparison with Google’s TimesFM
- MOIRAI benchmark outcomes.
- Why MOIRAI will revolutionize the TS forecasting subject.
Let’s get began.
I’ve launched AI Horizon Forecast, a publication specializing in time-series and progressive AI analysis. Subscribe here to broaden your horizons!
We described the challenges intimately right here. To recap, these are:
- Issue discovering public time-series knowledge — for coaching a time-series basis mannequin.
- Time-series knowledge are extremely heterogeneous — not like in NLP, the place knowledge have well-defined grammar and vocabulary.
- Time collection will be multivariate — not like in NLP, the place enter is one-dimensional.
- Time collection have completely different granularities — day by day, weekly, month-to-month, and many others.
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