Update README.md

Author: ryuta-yoshimatsu

README.md

@@ -12,7 +12,7 @@ Get started now!
 
 ## What's New
 
-- Feb 2025: [Prophet](https://www.sktime.net/en/stable/api_reference/auto_generated/sktime.forecasting.fbprophet.Prophet.html) is available for univariate forecasting via `SKTimeProphet`. Try the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/daily/local_univariate_daily).
+- Feb 2025: [Prophet](https://www.sktime.net/en/stable/api_reference/auto_generated/sktime.forecasting.fbprophet.Prophet.html) is available for univariate forecasting via `SKTimeProphet`. Try the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/daily/local_univariate_daily.py).
 - Feb 2025: Added a post evaluation notebook that shows how to run fine-grained model selection after running MMF. Try the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.ipynb).
 - Jan 2025: [TimesFM](https://github.com/google-research/timesfm) is available for univariate and covariate forecasting. Try the notebooks: [univariate](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/daily/foundation_daily.py) and [covariate](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/external_regressors/foundation_external_regressors_daily.py).
 - Jan 2025: [Chronos Bolt](https://github.com/amazon-science/chronos-forecasting) models are available for univariate forecasting. Try the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/daily/foundation_daily.py).