Time series forecasting is a key ingredient in the automation and optimization of business processes: in retail, deciding which products to order and where to store them depends on the forecasts of future demand in different regions; in cloud computing, the estimated future usage of services and infrastructure components guides capacity planning; and workforce scheduling in warehouses and factories requires forecasts of the future workload. Recent years have witnessed a paradigm shift in forecasting techniques and applications, from computer-assisted model- and assumption-based to data-driven and fully-automated. This shift can be attributed to the availability of large, rich, and diverse time series data sources and result in a set of challenges that need to be addressed, such as the following: How can we build statistical models to efficiently and effectively learn to forecast from large and diverse data sources? How can we leverage the statistical power of “similar” time series to improve forecasts in the case of limited observations? What are the implications for building forecasting systems that can handle large data volumes?

The objective of this tutorial is to provide a concise and intuitive overview of the most important methods and tools available for solving large-scale forecasting problems. We review the state of the art in classical modeling of time series and modern methods with a particular focus on deep learning for forecasting. Furthermore, we discuss practical aspects of forecasting, evaluation and provide example problems. Our focus is on providing an intuitive overview of the methods and practical issues which we will illustrate via case studies.


  • Introduction to Forecasting & Forecast Evaluations
  • Methods & Models For Forecasting
  • Forecasting with Neural Networks
  • Q&A and Pause

  • Generating Probabilistic Forecasts with Neural Nets
  • Deep Probabilistic Models
  • (if time permits) Multivariate Deep Probabilistic Models
  • (if time permits) Hierarchical Forecasting
  • Q&A and overflow




  Jan Gasthaus is Software Engineer at Meta, and a former Principal Machine Learning Scientist in the Amazon AI Labs, working mainly on time series forecasting and large-scale probabilistic machine learning. He is passionate about developing novel machine learning solutions for addressing challenging business problems with scalable machine learning systems, all the way from scientific ideation to productization. Prior to joining Amazon, Jan obtained a BS in Cognitive Science from the University of Osnabrueck, an MS in Intelligent Systems from UCL, and a PhD from the Gatsby Unit, UCL, focusing on Nonparametric Bayesian methods for sequence data.


  Tim Januschowski is the Director Pricing Platform at Zalando, SE. Prior, he was a Machine Learning Science Manager in Amazon AI Labs. He has worked on forecasting since starting his professional career. At Amazon, he has produced end-to-end solutions for a wide variety of forecasting problems, from demand forecasting to server capacity forecasting which he has continued to do at Zalando with a causal twist. Tim’s personal interests in forecasting span applications, system, algorithm and modeling aspects and the downstream mathematical programming problems. He studied Mathematics at TU Berlin, IMPA, Rio de Janeiro, and Zuse-Institute Berlin and holds a PhD from University College Cork. Tim is passionate about teachign students and professionals. He serves as a director at the International Institute of Forecasters.


  Yuyang (Bernie) Wang is a Principal Machine Learning Scientist in Amazon AI Labs, working mainly on large-scale probabilistic machine learning with its application in Forecasting. He received his PhD in Computer Science from Tufts University, MA, US and he holds an MS from the Department of Computer Science at Tsinghua University, Beijing, China. His research interests span statistical machine learning, numerical linear algebra, and random matrix theory. In forecasting, Yuyang has worked on all aspects ranging from practical applications to theoretical foundations.


Some recent tutorials by Christos Faloutsos and Co. on big time series mining:

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