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

Business forecasting using hybrid approach: A new forecasting method using deep learning and time series

13:45–14:25 Wednesday, 10 October 2018
Models and Methods
Location: Windsor Suite
Secondary topics:  Temporal data and time-series

Who is this presentation for?

  • Data scientists, CDOs, managers, applied AI experts, business analysts, data analysts, forecast experts, and gamblers

Prerequisite knowledge

  • A basic understanding of machine learning and forecasting

What you'll learn

  • Learn how to use diverse methods, including AI, for forecasting your business

Description

Machine learning methods are becoming increasingly popular among mainstream forecasting practitioners, due to their ability to obtain better accuracy when the forecast horizon is much longer and nonlinear. While this is true, an econometrics approach allows more transparency and formal standard to verify forecasts with better performance 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).

Photo of Pasi Helenius

Pasi Helenius

SAS

Pasi Helenius is a senior business solutions manager within the Artificial Intelligence Practice at SAS.

Photo of Larry Orimoloye

Larry Orimoloye

SAS

Larry Orimoloye is a senior business solutions manager within the Artificial Intelligence Practice at SAS.

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