Moirai 1.0 R Base Moirai, the Masked Encoder based Universal Time Series Forecasting Transformer is a Large Time Series Model pre trained on LOTSA data. For more details on the Moirai architecture, training, and results, please refer to the paper. Fig. 1: Overall architecture of Moirai. Visualized is a 3 variate time series, where variates 0 and 1 are target variables (i.e. to be forecasted, and variate 2 is a dynamic covariate (values in forecast horizon known). Based on a patch size of 64, each variate is patchified into 3 tokens. The patch embeddings along with sequence and variate id are fed into the Transformer. The shaded patches represent the forecast horizon to be forecasted, whose corresponding output representations are mapped into the mixture distribution parameters. Usage To perform inference with Moirai, install the uni2ts library from our GitHub repo. 1. Clone repository: 2) Create virtual environment: 3) Build from source: 4) Create a .env file: A simple example to get started: The Moirai Family Model Parameters : : : : Moirai 1.0 R Small 14m Moirai 1.0 R Base 91m Moirai 1.0 R Large 311m Citation If you're using Uni2TS in your research or applications, please cite it…
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