Business forecasting generally employs machine learning methods for longer and nonlinear use cases and econometrics approaches for linear trends. Pasi Helenius and Larry Orimoloye outline a hybrid approach that combines deep learning and econometrics. This method is particularly useful in areas such as competitive event (CE) forecasting (e.g., in sports events political events).

Personalization is a common theme in social networks and ecommerce businesses. Personalization at Uber involves an understanding of how each driver and rider is expected to behave on the platform. Ankit Jain explains how Uber employs deep learning using LSTMs and its huge database to understand and predict the behavior of each and every user on the platform.

Roger Barga, Sudipto Guha, and Kapil Chhabra explain how unsupervised learning with the robust random cut forest (RRCF) algorithm enables insights into streaming data and share new applications to impute missing values, forecast future values, detect hotspots, and perform classification tasks. They also demonstrate how to implement unsupervised learning over massive data streams.

Machine learning and data science systems often fail in production in unexpected ways. David Talby shares real-world case studies showing why this happens and explains what you can do about it, covering best practices and lessons learned from a decade of experience building and operating such systems at Fortune 500 companies across several industries.

Time series are everywhere around us. Understanding them requires taking into account the sequence of values seen in previous steps and even long-term temporal correlations. Join Bruno Gonalves to learn how to use recurrent neural networks to model and forecast time series and discover the advantages and disadvantages of recurrent neural networks with respect to more traditional approaches.

Personalization is a common theme in social networks and ecommerce businesses. Personalization at Uber involves an understanding of how each driver and rider is expected to behave on the platform. Ankit Jain explains how Uber employs deep learning using LSTMs and its huge database to understand and predict the behavior of each and every user on the platform.

Switzerland-based startup WinJi capitalizes on two current megatrends: big data and renewable energy. Stamatis Stefanakos offers an overview of WinJi's TruePower Asset Management Platform, covering the overall architecture and the motivation behind it, the physics behind the data, and the business case.

Oil exploration and production is technically challenging, and exploiting the associated data brings its own difficulties. Jane McConnell and Paul Ibberson share best practices and lessons learned helping oil companies modernize their data architecture and plan the IT/OT convergence required to benefit from full digitalization.

Christina Erlwein-Sayer explains how to enhance the modeling and forecasting of sovereign bond spreads by considering quantitative information gained from macroeconomic news sentiment, using a number of large news analytics datasets.

Time series forecasting and anomaly detection is of utmost importance at Uber. However, the scale of the problem, the need for speed, and the importance of accuracy make anomaly detection a challenging data science problem. Andrea Pasqua and Anny Chen explain how the use of recurrent neural networks is allowing Uber to meet this challenge.