Amazon Forecast is no longer available to new customers. Existing customers of Amazon Forecast can continue to use the service as normal. [Learn more"](https://aws.amazon.com/blogs/machine-learning/transition-your-amazon-forecast-usage-to-amazon-sagemaker-canvas/)

# Getting Started (Python Notebooks)
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**Note**  
For a complete list of tutorials using Python notebooks, see the Amazon Forecast [Github Samples](https://github.com/aws-samples/amazon-forecast-samples/tree/master/notebooks) page.

To get started using Amazon Forecast APIs with Python notebooks, see the [Getting Started Tutorial](https://github.com/aws-samples/amazon-forecast-samples/blob/main/notebooks/basic/Getting_Started/Amazon_Forecast_Quick_Start_Guide.ipynb). The tutorial guides you through the core steps of Forecast from start to finish.

For basic tutorials for specific processes, refer to the following Python notebooks:

1. [Preparing data](https://github.com/aws-samples/amazon-forecast-samples/blob/master/notebooks/basic/Tutorial/1.Importing_Your_Data.ipynb) - Prepare a dataset, create a dataset group, define the schema, and import the dataset group.

1. [Building your predictor](https://github.com/aws-samples/amazon-forecast-samples/blob/master/notebooks/basic/Tutorial/2.Building_Your_Predictor.ipynb) - Train a predictor on the data you imported into your Forecast dataset.

1. [Evaluating predictors](https://github.com/aws-samples/amazon-forecast-samples/blob/master/notebooks/basic/Tutorial/3.Evaluating_Your_Predictor.ipynb) - Obtain predictions, visualize predictions, and compare results.

1. [Retraining predictors](https://github.com/aws-samples/amazon-forecast-samples/blob/main/notebooks/advanced/Retraining_AutoPredictor/Retraining.ipynb) - Retrain an existing predictor with updated data.

1. [Upgrade to AutoPredictor](https://github.com/aws-samples/amazon-forecast-samples/blob/main/notebooks/basic/Upgrading_to_AutoPredictor/UpgradeToAutoPredictor.ipynb) - Upgrade legacy predictors to AutoPredictor.

1. [Clean Up](https://github.com/aws-samples/amazon-forecast-samples/blob/master/notebooks/basic/Tutorial/4.Cleanup.ipynb) - Delete the dataset groups, predictors, forecasts created during the tutorials.

To repeat the Getting Started tutorial with AutoML, see [Getting Started with AutoML](https://github.com/aws-samples/amazon-forecast-samples/blob/master/notebooks/advanced/Getting_started_with_AutoML/Getting_started_with_AutoML.ipynb).

## Advanced Tutorials
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For more advanced tutorials, refer to the following Python notebooks:
+ [Item-level Explainability](https://github.com/aws-samples/amazon-forecast-samples/blob/main/notebooks/advanced/Item_Level_Explainability/Item_Level_Explanability.ipynb) - Understand how dataset attributes impact forecasts for specific time series and time points.
+ [Comparing multiple models ](https://github.com/aws-samples/amazon-forecast-samples/blob/master/notebooks/advanced/Compare_Multiple_Models/Compare_Multiple_Models.ipynb) - Create predictors using Prophet, ETS, and DeepAR\$1, and compare their performances by visualizing the results.
+ [Cold start forecasting](https://github.com/aws-samples/amazon-forecast-samples/blob/master/notebooks/advanced/Forecast%20with%20Cold%20Start%20Items/Forecast%20with%20Cold%20Start%20Items.ipynb) - Use item metadata and the DeepAR\$1 algorithm to forecast for cold-start scenarios (when there is little to no historical data).
+ [Incorporating related time-series datasets](https://github.com/aws-samples/amazon-forecast-samples/blob/master/notebooks/advanced/Incorporating_Related_Time_Series_dataset_to_your_Predictor/Incorporating_Related_Time_Series_dataset_to_your_Predictor.ipynb) - Use related time-series datasets to improve the accuracy of your model.
+ [Incorporating item metadata](https://github.com/aws-samples/amazon-forecast-samples/blob/master/notebooks/advanced/Incorporating_Item_Metadata_Dataset_to_your_Predictor/Incorporating_Item_Metadata_Dataset_to_your_Predictor.ipynb) - Use item metadata to improve the accuracy of your model.
+ [Using the Weather Index](https://github.com/aws-samples/amazon-forecast-samples/tree/master/notebooks/advanced/Weather_index) - Use the Weather Index to incorporate historical and projected weather information when training your predictors.
+ [Performing What-if analysis](https://github.com/aws-samples/amazon-forecast-samples/blob/master/notebooks/advanced/WhatIf_Analysis/WhatIf_Analysis.ipynb) - Explore different pricing scenarios and evaluate how it impacts demand.
+ [Evaluate item-level accuracy](https://github.com/aws-samples/amazon-forecast-samples/blob/master/notebooks/advanced/Item_Level_Accuracy/Item_Level_Accuracy_Using_Bike_Example.ipynb) - Export backtest metrics and forecasts, and evaluate the item-level performance of your predictor.