Are there any tutorials for integrating DeepAR with TensorFlow

1. TensorFlow Implementation of DeepAR:
- This GitHub repository provides a TensorFlow implementation of the DeepAR algorithm. It includes examples of how to fit a univariate time series and plot the results with uncertainty bands[1].

2. TensorFlow Tutorials:
- TensorFlow offers a comprehensive set of tutorials that cover various aspects of machine learning, including time series forecasting. These tutorials include examples of building different types of models, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), to perform time series forecasting[3].

3. Time Series Forecasting with TensorFlow:
- This tutorial on TensorFlow's official website provides an introduction to time series forecasting using TensorFlow. It covers building different styles of models, including CNNs and RNNs, and includes examples of how to implement these models[4].

4. DeePar Framework:
- DeePar is a hybrid device-edge-cloud execution framework designed for mobile deep learning applications. It proposes a layer-level partitioning strategy to distribute the computation loads among the device, edge server, and cloud. This framework can be used to optimize the inference performance of DeepAR models[5].

These resources provide detailed instructions and examples for integrating DeepAR with TensorFlow, enabling users to leverage the strengths of both frameworks for advanced time series forecasting applications.