Forecasting user activities is one of the most common problems that business groups face. Various industries, such as cloud solution providers and online retailer, often need to forecast product consumptions and user engagement. The data scientist team under Azure Identity faces same challenges. With billions of user activities on the Azure cloud, being able to accurately forecast user behavior becomes essential in business planning. In this talk, we focus on forecasting the customer usage in daily basis. In this user case, statistical time series models and machine learning approaches like tree-based models failed to generate forecasts with satisfactory accuracies. Recently developed deep learning models could boost the performance yet they are hard to implement.
We will introduce how we improve 25% of forecasting accuracy with dilated convolutional neural networks (CNNs) and reduce 80% of the time in development with the best practices of time series forecasting. Dilated CNNs are recently proposed for modeling sequence data. They can achieve state-of-the-art performance in time series forecasting and are easier to train compared with recurrent neural networks. To speed up the model development process, we leverage the best practices of time series forecasting that another Microsoft team has created. This is a framework that provides standard workflows of various methods, e.g., statistical methods, traditional machine learning methods, and recently developed deep learning approaches. It also includes reusable utility functions for data standardization and feature engineering. With such utility functions, we convert our dataset into a standard format. Then, we implement the Dilated CNN model and other baseline models by following the templates in the framework. Moreover, we tune the hyperparameters of each model with HyperDrive in Azure Machine Learning to achieve a fair comparison of model performance in terms of accuracy, running time, and cost.
Tao Lu is a Data Scientist in the Cloud and AI organization at Microsoft. He has strong background in applying machine learning and deep learning techniques to forecasting problems. He has deep domain knowledge in cloud identity and financial services industry. He graduated from University of Washington with a master degree in Computational Finance.
Chenhui Hu is a Data Scientist in the Cloud AI organization at Microsoft. His current interests include retail forecast, inventory optimization, IoT data, and deep learning. He received his PhD degree from Harvard University with his PhD thesis focusing on biomedical imaging data mining. He also has research experience in wireless networks and network data analysis. He is a recipient of the third IEEE ComSoc Asia-Pacific Outstanding Paper Award.
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