From c54ae4379075707206319ca88d4e526f68fec707 Mon Sep 17 00:00:00 2001 From: geetu040 Date: Tue, 16 Jul 2024 15:10:52 +0200 Subject: [PATCH 1/2] use link instead of block --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 0191b86..e56e0b0 100644 --- a/README.md +++ b/README.md @@ -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 From 1311ad38681e768ed8d23b7a67301719c1390c9d Mon Sep 17 00:00:00 2001 From: geetu040 Date: Wed, 17 Jul 2024 09:11:12 +0200 Subject: [PATCH 2/2] use github links instead --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index e56e0b0..f34c228 100644 --- a/README.md +++ b/README.md @@ -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](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](https://github.com/google-research/timesfm/blob/master/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](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](https://github.com/google-research/timesfm/blob/master/notebooks/covariates.ipynb). ## Finetuning -We have provided an example of finetuning the model on a new dataset in [notebooks/finetuning.ipynb](notebooks/finetuning.ipynb). +We have provided an example of finetuning the model on a new dataset in [notebooks/finetuning.ipynb](https://github.com/google-research/timesfm/blob/master/notebooks/finetuning.ipynb). ## Contribution Style guide