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@@ -26,7 +26,7 @@ can be easily fine-tuned for your target data. Refer to our [paper](https://arxi
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  - TTM (1024-96, released in this model card with 1M parameters) outperforms pre-trained MOIRAI (Small, 14M parameters) by 10%, MOIRAI (Base, 91M parameters) by 4% and
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  MOIRAI (Large, 311M parameters) by 3% on zero-shot forecasting (fl = 96). (TODO: add notebook)
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  - TTM quick fine-tuning also outperforms the hard statistical baselines (Statistical ensemble and S-Naive) in
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- M4-hourly dataset which pretrained TS models are finding hard to outperform. (TODO: add notebook)
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  - TTM takes only a *few seconds for zeroshot/inference* and a *few minutes for finetuning* in 1 GPU machine, as
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  opposed to long timing-requirements and heavy computing infra needs of other existing pretrained models.
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  - TTM (1024-96, released in this model card with 1M parameters) outperforms pre-trained MOIRAI (Small, 14M parameters) by 10%, MOIRAI (Base, 91M parameters) by 4% and
27
  MOIRAI (Large, 311M parameters) by 3% on zero-shot forecasting (fl = 96). (TODO: add notebook)
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  - TTM quick fine-tuning also outperforms the hard statistical baselines (Statistical ensemble and S-Naive) in
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+ M4-hourly dataset which existing pretrained TS models are finding hard to outperform. (TODO: add notebook)
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  - TTM takes only a *few seconds for zeroshot/inference* and a *few minutes for finetuning* in 1 GPU machine, as
31
  opposed to long timing-requirements and heavy computing infra needs of other existing pretrained models.
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