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README.md
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README.md
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@ -9,21 +9,26 @@ The examples and best practices are provided as [Python Jupyter notebooks and R
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## Content
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## Content
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The following is a summary of models or methods for developing forecasting solutions covered in this repository. The [examples](examples) are organized according to use cases. Currently, we focus on a retail sales forecasting use case as it is widely used in [assortment planning](https://repository.upenn.edu/cgi/viewcontent.cgi?article=1569&context=edissertations), [inventory optimization](https://en.wikipedia.org/wiki/Inventory_optimization), and [price optimization](https://en.wikipedia.org/wiki/Price_optimization).
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The following is a summary of models and methods for developing forecasting solutions covered in this repository. The [examples](examples) are organized according to use cases. Currently, we focus on a retail sales forecasting use case as it is widely used in [assortment planning](https://repository.upenn.edu/cgi/viewcontent.cgi?article=1569&context=edissertations), [inventory optimization](https://en.wikipedia.org/wiki/Inventory_optimization), and [price optimization](https://en.wikipedia.org/wiki/Price_optimization). To enable high-throughput forecasting scenarios, we have included examples for forecasting multiple time series with distributed training techniques such as Ray in Python, parallel package in R, and multi-threading in LightGBM.
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| Model/Method | Language | Type | Description |
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| Model | Language | Description |
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|---------------------|----------|----------------------------|-------------------------------------------------------------------------------------------------------------|
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|---------------------------------------------------------------------------------------------------|----------|-------------------------------------------------------------------------------------------------------------|
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| Auto ARIMA | Python | Statistical | Auto Regressive Integrated Moving Average (ARIMA) model that is automatically selected |
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| [Auto ARIMA](examples/grocery_sales/python/00_quick_start/autoarima_single_round.ipynb) | Python | Auto Regressive Integrated Moving Average (ARIMA) model that is automatically selected |
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| Linear Regression | Python | Classical Machine Learning | Linear regression model trained on lagged features of the target variable and external features |
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| [Linear Regression](examples/grocery_sales/python/00_quick_start/azure_automl_single_round.ipynb) | Python | Linear regression model trained on lagged features of the target variable and external features |
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| LightGBM | Python | Classical Machine Learning | Gradient boosting decision tree implemented with LightGBM package for high accuracy and fast speed |
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| [LightGBM](examples/grocery_sales/python/00_quick_start/lightgbm_single_round.ipynb) | Python | Gradient boosting decision tree implemented with LightGBM package for high accuracy and fast speed |
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| DilatedCNN | Python | Deep Learning | Dilated Convolutional Neural Network that captures long-range temporal flow with dilated causal connections |
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| [DilatedCNN](examples/grocery_sales/python/02_model/dilatedcnn_multi_round.ipynb) | Python | Dilated Convolutional Neural Network that captures long-range temporal flow with dilated causal connections |
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| AutoML | Python | AzureML | AzureML service that automates model development process and identifies the best machine learning pipeline |
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| [Mean Forecast](examples/grocery_sales/R/02_basic_models.Rmd) | R | Simple forecasting method based on historical mean |
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| HyperDrive | Python | AzureML | Azure ML service for tuning hyperparameters of machine learning models in parallel on cloud |
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| [ARIMA](examples/grocery_sales/R/02a_reg_models.Rmd) | R | ARIMA model without or with external features |
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| AzureML Web Service | Python | AzureML | Azure ML service for deploying a model as a web service on Azure Container Instances |
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| [ETS](examples/grocery_sales/R/02_basic_models.Rmd) | R | Exponential Smoothing algorithm with additive errors |
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| Mean Forecast | R | Statistical | Simple forecasting method based on historical mean |
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| [Prophet](examples/grocery_sales/R/02b_prophet_models.Rmd) | R | Automated forecasting procedure based on an additive model with non-linear trends |
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| ARIMA | R | Statistical | ARIMA model without or with external features |
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| ETS | R | Statistical | Exponential Smoothing algorithm with additive errors |
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The repository also comes with AzureML-themed notebooks and best practices recipes to accelerate the development of scalable, production-grade forecasting solutions on Azure. In particular, we have the following examples for forecasting with Azure AutoML as well as tuning and deploying a forecasting model on Azure.
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| Prophet | R | Statistical | Automated forecasting procedure based on an additive model with non-linear trends |
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| Method | Language | Description |
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|-----------------------------------------------------------------------------------------------------------|----------|------------------------------------------------------------------------------------------------------------|
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| [Azure AutoML](examples/grocery_sales/python/00_quick_start/azure_automl_single_round.ipynb) | Python | AzureML service that automates model development process and identifies the best machine learning pipeline |
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| [HyperDrive](examples/grocery_sales/python/03_model_tune_deploy/azure_hyperdrive_lightgbm.ipynb) | Python | AzureML service for tuning hyperparameters of machine learning models in parallel on cloud |
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| [AzureML Web Service](examples/grocery_sales/python/03_model_tune_deploy/azure_hyperdrive_lightgbm.ipynb) | Python | AzureML service for deploying a model as a web service on Azure Container Instances |
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## Getting Started in Python
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## Getting Started in Python
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