Moirai is a large pre-trained Time Series Model based on the Masked Encoder architecture. It is a universal time series forecasting model capable of addressing diverse forecasting tasks across multiple domains, frequencies, and variables in a zero-shot manner.
This is a version of Moirai small trained by Faculty AI. It was pre-trained on the LOTSA data using the codebase provided by Woo et al. (2024). Both the dataset and codebase are licensed under the Apache License 2.0. For more details on the model architecture, training, and results, please refer to the paper.
Usage
Please follow the Installation instructions and Getting Started section provided in the uni2ts repo. To use the model trained by Faculty AI simply use FacultyAI/moirai-small when fetching the model weights.
model = MoiraiForecast(
module=MoiraiModule.from_pretrained("FacultyAI/moirai-small"),
...
)
References
Woo, G., Liu, C., Kumar, A., Xiong, C., Savarese, S., & Sahoo, D. (2024). Unified Training of Universal Time Series Forecasting Transformers. arXiv preprint arXiv:2402.02592.