Deep Learning: the final Frontier for Time Series Analysis?
It’s not a secret that deep learning already made a revolution in several perception fields as vision, language and speech understanding and keeps pushing the frontiers. Take a tour of the final frontier for time series analysis in this advanced development session from the Machine Learning Conference.
One important data type which includes time series, digital signals and any sequential observations is still mainly processed with rather standard mathematical and algorithmic routines. In this talk, we will review, what are the main sources of time series in the world, what are the “basic” algorithms and how exactly they might be improved and replaced with different neural network architectures.
Apart from the models’ details, we will also study the typical tasks that have to be solved while working with time series: classification, prediction, anomaly detection, simulation and others and exactly deep learning can be leveraged to solve them on the state-of-the-art level.
Some previous experience with time series/signal processing is useful for getting the most out of this session, but not required.
Alex Honchar is developing production-ready AI solutions for small and medium businesses for the last 5 years, giving public speeches in Europe and blogging about ML and AI recent advances. His focus applications are time series analysis, NLP and computer vision in MedTech and Fintech domains. He is co-founder and CTO at Neurons Lab and co-founder and architect at Mawi Solutions. Alex’s mission is to build AI products, that truly outperform human skills or deliver new knowledge that couldn’t be discovered solely with human intellect. He believes that such products should not leave professionals without their jobs, but the opposite – become valuable partners for achieving more ambitious goals.