Main

News:

• JMLR Proceedings are now available here.
• Thank you to all the participants of NIPS Time Series Workshop 2016! We are preparing for NIPS Time Series Workshop 2017 and look forward to hosting you next year in Long Beach, California!
• Congratulations to Christopher Xie for the Best Oral Presentation on A Unified Framework for Missing Data and Cold Start Prediction for Time Series Data.
  • Congratulations to Mahdi Karami for the Best Poster Presentation on Optimal Linear Dynamical System Identification.
• Keynote talks slides are available on Schedule page.
• Ask and vote on the questions you would like to ask our panel on tricider.

Overview:

Data, in the form of time-dependent sequential observations emerge in many key real-world problems, ranging from biological data, financial markets, weather forecasting to audio/video processing. However, despite the ubiquity of such data, most mainstream machine learning algorithms have been primarily developed for settings in which sample points are drawn i.i.d. from some (usually unknown) fixed distribution. While there exist algorithms designed to handle non-i.i.d. data, these typically assume specific parametric form for the data-generating distribution. Such assumptions may undermine the complex nature of modern data which can possess long-range dependency patterns, and for which we now have the computing power to discern. On the other extreme lie on-line learning algorithms that consider a more general framework without any distributional assumptions. However, by being purely-agnostic, common on-line algorithms may not fully exploit the stochastic aspect of time-series data.