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8-9 Oct 2018: Training
9-11 Oct 2018: Tutorials & Conference
London, UK

Schedule: Temporal data and time-series sessions

13:30–17:00 Tuesday, 9 October 2018
Models and Methods
Location: Buckingham Room - Palace Suite
Yijing Chen (Microsoft), Dmitry Pechyoni (Microsoft), Angus Taylor (Microsoft), Vanja Paunic (Microsoft)
Average rating: ***..
(3.67, 3 ratings)
Buisnesses use forecasting to make better decisions and allocate resources more effectively. Recurrent neural networks (RNNs) have achieved a lot of success in text, speech, and video analysis but are less used for time series forecasting. Join Yijing Chen, Dmitry Pechyoni, Angus Taylor, and Vanja Paunic to learn how to apply RNNs to time series forecasting. Read more.
13:45–14:25 Wednesday, 10 October 2018
Models and Methods
Location: Windsor Suite
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). Read more.
14:35–15:15 Wednesday, 10 October 2018
Models and Methods
Location: Windsor Suite
Andrea Pasqua (Uber)
Andrea Pasqua investigates the merits of using deep learning and other machine learning approaches in the area of forecasting and describes some of the machine learning approaches Uber uses to forecast time series of business relevance. Read more.
16:00–16:40 Wednesday, 10 October 2018
Implementing AI
Location: Windsor Suite
Gaurav Chakravorty explains how recommender systems can be utilized for investment management and details how AI and deep learning are used in trading today. Read more.
11:05–11:45 Thursday, 11 October 2018
Models and Methods
Location: King's Suite - Balmoral
Vitaly Kuznetsov (Google), Zelda Mariet (MIT)
Vitaly Kuznetsov and Zelda Mariet compare sequence-to-sequence modeling to classical time series models and provide the first theoretical analysis of a framework that uses sequence-to-sequence models for time series forecasting. Read more.
11:55–12:35 Thursday, 11 October 2018
Implementing AI
Location: Hilton Meeting Room 3-6
Aileen Nielsen (Skillman Consulting)
Average rating: *****
(5.00, 2 ratings)
Deep learning for time series prediction has made rapid progress in the past few years, but performance still greatly lags that of other intelligence tasks. Aileen Nielsen offers an overview of the state of the art in 2018, covering the hottest new architectures, emerging best practices for RNN training, and long overdue standard metrics to measure and compete on neural network prediction. Read more.
16:00–16:40 Thursday, 11 October 2018
Ira Cohen (Anodot)
With the more applications of machine learning-based applications, the complex algorithms that automate behaviors can get out of control. Ira Cohen explains how to catch problems and glitches early on by using machine learning algorithms to monitor these algorithms for anomalous behavior. Read more.
16:50–17:30 Thursday, 11 October 2018
Impact of AI on Business and Society
Location: King's Suite - Balmoral
Johnnie Ball (Fluidly)
Average rating: *****
(5.00, 1 rating)
Cashflow is responsible for 80–90% of UK SME failure. Fluidly uses the wealth of financial data available through APIs to instantly predict cashflow. Johnnie Ball details how the company built an automated cashflow engine, explores the challenges faced in applying AI to financial data, and explains how machine learning can redefine how we think about established approaches to modeling. Read more.