Moirai 2.0: When Less Is More for Time Series Forecasting
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917243228520448 |
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| author | Liu, Chenghao Aksu, Taha Liu, Juncheng Liu, Xu Yan, Hanshu Pham, Quang Savarese, Silvio Sahoo, Doyen Xiong, Caiming Li, Junnan |
| author_facet | Liu, Chenghao Aksu, Taha Liu, Juncheng Liu, Xu Yan, Hanshu Pham, Quang Savarese, Silvio Sahoo, Doyen Xiong, Caiming Li, Junnan |
| contents | We introduce Moirai 2.0, a decoder-only time-series foundation model trained on a new corpus of 36M series. The model adopts quantile forecasting and multi-token prediction, improving both probabilistic accuracy and inference efficiency. On the Gift-Eval benchmark, it ranks among the top pretrained models while achieving a strong trade-off between accuracy, speed, and model size. Compared to Moirai 1.0, Moirai 2.0 replaces masked-encoder training, multi-patch inputs, and mixture-distribution outputs with a simpler decoder-only architecture, single patch, and quantile loss. Ablation studies isolate these changes -- showing that the decoder-only backbone along with recursive multi-quantile decoding contribute most to the gains. Additional experiments show that Moirai 2.0 outperforms larger models from the same family and exhibits robust domain-level results. In terms of efficiency and model size, Moirai 2.0 is twice as fast and thirty times smaller than its prior best version, Moirai 1.0-Large, while also performing better. Model performance plateaus with increasing parameter count and declines at longer horizons, motivating future work on data scaling and long-horizon modeling. We release code and evaluation details to support further research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_11698 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Moirai 2.0: When Less Is More for Time Series Forecasting Liu, Chenghao Aksu, Taha Liu, Juncheng Liu, Xu Yan, Hanshu Pham, Quang Savarese, Silvio Sahoo, Doyen Xiong, Caiming Li, Junnan Machine Learning We introduce Moirai 2.0, a decoder-only time-series foundation model trained on a new corpus of 36M series. The model adopts quantile forecasting and multi-token prediction, improving both probabilistic accuracy and inference efficiency. On the Gift-Eval benchmark, it ranks among the top pretrained models while achieving a strong trade-off between accuracy, speed, and model size. Compared to Moirai 1.0, Moirai 2.0 replaces masked-encoder training, multi-patch inputs, and mixture-distribution outputs with a simpler decoder-only architecture, single patch, and quantile loss. Ablation studies isolate these changes -- showing that the decoder-only backbone along with recursive multi-quantile decoding contribute most to the gains. Additional experiments show that Moirai 2.0 outperforms larger models from the same family and exhibits robust domain-level results. In terms of efficiency and model size, Moirai 2.0 is twice as fast and thirty times smaller than its prior best version, Moirai 1.0-Large, while also performing better. Model performance plateaus with increasing parameter count and declines at longer horizons, motivating future work on data scaling and long-horizon modeling. We release code and evaluation details to support further research. |
| title | Moirai 2.0: When Less Is More for Time Series Forecasting |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.11698 |