diff --git a/docs/source/en/model_doc/autoformer.md b/docs/source/en/model_doc/autoformer.md
index f50705d0e3e..20977c71cae 100644
--- a/docs/source/en/model_doc/autoformer.md
+++ b/docs/source/en/model_doc/autoformer.md
@@ -29,6 +29,12 @@ The abstract from the paper is the following:
 This model was contributed by [elisim](https://huggingface.co/elisim) and [kashif](https://huggingface.co/kashif).
 The original code can be found [here](https://github.com/thuml/Autoformer).
 
+## Resources
+
+A list of official Hugging Face and community (indicated by 🌎) resources to help you get started. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
+
+- Check out the Autoformer blog-post in HuggingFace blog: [Yes, Transformers are Effective for Time Series Forecasting (+ Autoformer)](https://huggingface.co/blog/autoformer)
+
 ## AutoformerConfig
 
 [[autodoc]] AutoformerConfig
@@ -43,4 +49,4 @@ The original code can be found [here](https://github.com/thuml/Autoformer).
 ## AutoformerForPrediction
 
 [[autodoc]] AutoformerForPrediction
-    - forward
\ No newline at end of file
+    - forward
diff --git a/docs/source/en/model_doc/informer.md b/docs/source/en/model_doc/informer.md
index 4ab48f6f828..0d2d82a3f57 100644
--- a/docs/source/en/model_doc/informer.md
+++ b/docs/source/en/model_doc/informer.md
@@ -29,7 +29,10 @@ The abstract from the paper is the following:
 This model was contributed by [elisim](https://huggingface.co/elisim) and [kashif](https://huggingface.co/kashif).
 The original code can be found [here](https://github.com/zhouhaoyi/Informer2020).
 
-Tips:
+## Resources
+
+A list of official Hugging Face and community (indicated by 🌎) resources to help you get started. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
+
 - Check out the Informer blog-post in HuggingFace blog: [Multivariate Probabilistic Time Series Forecasting with Informer](https://huggingface.co/blog/informer)
 
 ## InformerConfig
diff --git a/docs/source/en/model_doc/time_series_transformer.md b/docs/source/en/model_doc/time_series_transformer.md
index f20387fb3ad..208798aa1c6 100644
--- a/docs/source/en/model_doc/time_series_transformer.md
+++ b/docs/source/en/model_doc/time_series_transformer.md
@@ -29,7 +29,6 @@ The Time Series Transformer model is a vanilla encoder-decoder Transformer for t
 
 Tips:
 
-- Check out the Time Series Transformer blog-post in HuggingFace blog: [Probabilistic Time Series Forecasting with 🤗 Transformers](https://huggingface.co/blog/time-series-transformers)
 - Similar to other models in the library, [`TimeSeriesTransformerModel`] is the raw Transformer without any head on top, and [`TimeSeriesTransformerForPrediction`]
 adds a distribution head on top of the former, which can be used for time-series forecasting. Note that this is a so-called probabilistic forecasting model, not a
 point forecasting model. This means that the model learns a distribution, from which one can sample. The model doesn't directly output values.
@@ -60,6 +59,12 @@ which is then fed to the decoder in order to make the next prediction (also call
 
 This model was contributed by [kashif](https://huggingface.co/kashif).
 
+## Resources
+
+A list of official Hugging Face and community (indicated by 🌎) resources to help you get started. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
+
+- Check out the Time Series Transformer blog-post in HuggingFace blog: [Probabilistic Time Series Forecasting with 🤗 Transformers](https://huggingface.co/blog/time-series-transformers)
+
 
 ## TimeSeriesTransformerConfig