The repository also comes with Azure Machine Learning (Azure ML) themed notebooks and best practices recipes to accelerate the development of scalable, production-grade forecasting solutions on Azure. You will find the following examples for forecasting with Azure AutoML as well as tuning and deploying a forecasting model on Azure. Developing an accurate forecasting solution can be a complex and time-consuming process. We hope the forecasting repo will help shorten your development cycle on Azure.

Time series forecasting is one of the most important topics in data science. Almost every business needs to predict the future in order to make better decisions and allocate resources more effectively. This repository provides examples and best practice guidelines for building forecasting solutions. The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in forecasting algorithms to build solutions and operationalize them. Rather than creating implementations from scratch, we draw from existing state-of-the-art libraries and build additional utilities around processing and featurizing the data, optimizing and evaluating models, and scaling up to the cloud.

Machine learning has become a vital component to get solutions in everyday life. It is adding intelligence in every product we are using today. Marketing software and demand forecasting are using ML to a great extent. In the latest generation, the data is available in bulk, but we need more tools to handle this data. Machine learning is the only solution to so this task as it allows the computer to learn from data for improved analysis.

Revenue forecasting is required by most enterprises for strategic business planning and for providing expected future results to investors. However, revenue forecasting processes in most companies are time-consuming and error-prone as they are performed manually by hundreds of financial analysts. In this paper, we present a novel machine learning based revenue forecasting solution that we developed to forecast 100% of Microsoft's revenue (around $85 Billion in 2016), and is now deployed into production as an end-to-end automated and secure pipeline in Azure. Our solution combines historical trend and seasonal patterns with additional information, e.g., sales pipeline data, within a unified modeling framework. In this paper, we describe our framework including the features, method for hyperparameters tuning of ML models using time series cross-validation, and generation of prediction intervals. We also describe how we architected an end-to-end secure and automated revenue forecasting solution on Azure using Cortana Intelligence Suite. Over consecutive quarters, our machine learning models have continuously produced forecasts with an average accuracy of 98-99 percent for various divisions within Microsoft's Finance organization. As a result, our models have been widely adopted by them and are now an integral part of Microsoft's most important forecasting processes, from providing Wall Street guidance to managing global sales performance.