Pushing the Limits of Recurrent Neural Networks
In predicting the movement of prices of correlated securities there are two vexing questions: reliability of predictions and model tuning including the number of layers to use in a deep learning model. We take a deeper dive into how to output only confident predictions in a dynamic fashion, and how to dynamically allocate the number of layers in each time and sequence. The results are discussed on financial market data from an investment firm. The audience will learn about state-of-the-art models and techniques, and we will share a bag-of-tricks to use.
Diego Klabjan is a professor at Northwestern University, Department of Industrial Engineering and Management Sciences. He is also Founding Director, Master of Science in Analytics, and the Deep Learning Lab. His expertise is focused on data science and deep learning with a concentration in finance, insurance, and healthcare. Professor Klabjan has led projects with large companies such as The Chicago Mercantile Exchange Group, Intel, General Motors and many other, and he is also assisting numerous start-ups with their analytics needs. He is also a founder of Opex Analytics.