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@@ -24,7 +24,7 @@ can be easily fine-tuned for your target data. Refer to our [paper](https://arxi
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  - Zero-shot results of TTM surpass the *few-shot results of many popular SOTA approaches* including
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  PatchTST (ICLR 23), PatchTSMixer (KDD 23), TimesNet (ICLR 23), DLinear (AAAI 23) and FEDFormer (ICML 22).
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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 forecast length 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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  - Zero-shot results of TTM surpass the *few-shot results of many popular SOTA approaches* including
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  PatchTST (ICLR 23), PatchTSMixer (KDD 23), TimesNet (ICLR 23), DLinear (AAAI 23) and FEDFormer (ICML 22).
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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