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@@ -202,7 +202,7 @@ forecast_df = tfm.forecast_on_df(
## Covariates Support
We now have an external regressors library on top of TimesFM that can support static covariates as well as dynamic covariates available in the future. We have an usage example in `notebooks/covariates.ipynb`.
We now have an external regressors library on top of TimesFM that can support static covariates as well as dynamic covariates available in the future. We have an usage example in [notebooks/covariates.ipynb](notebooks/covariates.ipynb).
Let's take a toy example of forecasting sales for a grocery store:
@@ -241,11 +241,11 @@ In this example, besides the `Daily_sales`, we also have covariates `Category`,
**Notice:** Here we make it mandatory that the dynamic covariates need to cover both the forecasting context and horizon. For example, all dynamic covariates in the example have 14 values: the first 7 correspond to the observed 7 days, and the last 7 correspond to the next 7 days.
We can now provide the past data of the two products along with static and dynamic covariates as a batch input to TimesFM and produce forecasts that take into the account the covariates. To learn more, check out the example in `notebooks/covariates.ipynb`.
We can now provide the past data of the two products along with static and dynamic covariates as a batch input to TimesFM and produce forecasts that take into the account the covariates. To learn more, check out the example in [notebooks/covariates.ipynb](notebooks/covariates.ipynb).
## Finetuning
We have provided an example of finetuning the model on a new dataset in `notebooks/finetuning.ipynb`.
We have provided an example of finetuning the model on a new dataset in [notebooks/finetuning.ipynb](notebooks/finetuning.ipynb).
## Contribution Style guide