TSW 2015 - 2015 Time Series Workshop

NIPS 2015 Time Series Workshop
December 11th, 2015
Montreal, Canada
https://sites.google.com/site/nipsts2015/home
Important dates:
Submission deadline: October 10th, 2015.
Acceptance decisions: October 24th, 2015.
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Overview:
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.
Submissions:
We solicit submission of unpublished research papers in the following areas:
* time series prediction
* time series classification
* clustering
* anomaly and change point detection
* correlation discovery
* dimensionality reduction
* online learning and time series analysis
* general theory for learning with stochastic processes
We invite researchers from the related areas of batch and online learning, reinforcement learning, data analysis and statistics, econometrics, and many other to join and contribute to this workshop.
Submission must adhere to NIPS 2015 style format available. Papers may be only up to 4 pages long, including figures. An additional fifth page containing only cited references is allowed. Supplementary material can be provided. We leave it up to the authors to decide whether to blind their submission or not. Please submit your manuscripts to nipstimeseries2015-AT-gmail.com.
All accepted papers will have a poster presentation and the best papers will be selected for an oral presentation.
Invited Speakers:
Emily Fox, University of Washington
Ramazan Gencay, Simon Fraser University
Shie Mannor, Technion - Israel Insitute of Technology
Mehryar Mohri, Courant Institute and Google Research
Organizers:
Oren Anava, Technion - Israel Insitute of Technology
Azadeh Khaleghi, Lancaster University
Vitaly Kuznetsov, Courant Institute
Alexander Rakhlin, University of Pennsylvania, The Wharton School