Update README.md

README.md

@@ -31,7 +31,7 @@ can be easily fine-tuned for your target data. Refer to our [paper](https://arxi
 - Zero-shot results of TTM surpass the *few-shot results of many popular SOTA approaches* including
   PatchTST (ICLR 23), PatchTSMixer (KDD 23), TimesNet (ICLR 23), DLinear (AAAI 23) and FEDFormer (ICML 22).
 - 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 2% and
-  MOIRAI-Large (311M parameters) by 3% on zero-shot forecasting (fl = 96). [[notebook]](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/tinytimemixer/ttm_benchmarking_1024_96.ipynb)
+  MOIRAI-Large (311M parameters) by 3% on zero-shot forecasting (horizon = 96). [[notebook]](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/tinytimemixer/ttm_benchmarking_1024_96.ipynb)
 - TTM quick fine-tuning also outperforms the competitive statistical baselines (Statistical ensemble and S-Naive) in
   M4-hourly dataset which existing pretrained TS models are finding difficult to outperform. [[notebook]](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/tinytimemixer/ttm_m4_hourly.ipynb)
 - TTM takes only a *few seconds for zeroshot/inference* and a *few minutes for finetuning* in 1 GPU machine, as