BIDS member Brett Naul discusses machine learning for time series data at SciPy 2016 in Austin, TX.
The analysis of time series data is a fundamental part of many scientific disciplines, but there are few resources meant to help domain scientists to easily explore time course datasets: traditional statistical models of time series are often too rigid to explain complex time domain behavior, while popular machine learning packages deal almost exclusively with "fixed-width" datasets containing a uniform number of features. Cesium is a time series analysis framework consisting of a Python library as well as a web front-end interface that allows researchers to apply modern machine learning techniques to time series data in a way that is simple, easily reproducible, and extensible.