Data, in the form of time-dependent sequential observations emerge in many key real-world problems ranging from biological data, to financial markets, to weather forecasting and audio/video processing. However, despite the ubiquity of such data, the vast majority of learning algorithms have been primarily developed for the setting in which sample points are drawn i.i.d. from some possibly unknown fixed distribution. While there exist algorithms designed to handle non-i.i.d. data, these typically assume specific parametric form of data-generating distribution. Such assumptions may undermine the possibly complex nature of modern data which can possess long-range dependency patterns that we now have the computing power to discern. On the other extreme are on-line learning algorithms that consider a more general framework without any distributional assumptions. However, common on-line algorithms may not fully address the stochastic aspect of time-series data.
The goal of this workshop is to bring together theoretical and applied researchers interested in the analysis of time series and development of new algorithms to process sequential data. This includes algorithms for time series prediction, classification, clustering, anomaly and change point detection, correlation discovery, dimensionality reduction as well as a general theory for learning and comparing stochastic processes. We invite researchers from the related areas of batch and online learning, reinforcement learning, data analysis and statistics, econometrics, and many others to contribute to this workshop.
Location and Dates:
This workshop will take place on December 11th after NIPS 2015 conference which is held in Montreal, Canada this year. The venue is Palais des Congrès de Montréal, room 514bc.
Oren Anava, Technion - Israel Insitute of Technology
Azadeh Khaleghi, Mathematics & Statistics, Lancaster University
Vitaly Kuznetsov, Courant Institute
Alexander Rakhlin, University of Pennsylvania, The Wharton School