University of Cambridge > > Isaac Newton Institute Seminar Series > Small Big Data: Temporal structure in discrete time series

Small Big Data: Temporal structure in discrete time series

Add to your list(s) Download to your calendar using vCal

  • UserIoannis Kontoyiannis (University of Cambridge; Athens University of Economics and Business)
  • ClockThursday 18 January 2018, 11:45-12:30
  • HouseSeminar Room 1, Newton Institute.

If you have a question about this talk, please contact INI IT.

STSW01 - Theoretical and algorithmic underpinnings of Big Data

The identification of useful temporal structure in discrete time series is an important component of algorithms used for many tasks in statistical inference and machine learning. Most of the early approaches developed were ineffective in practice, because the amount of data required for reliable modeling grew exponentially with memory length. On the other hand, many of the more modern methodological approaches that make use of more flexible and parsimonious models result in algorithms that do not scale well and are computationally ineffective for larger data sets.

We will discuss a class of novel methodological tools for effective Bayesian inference and model selection for general discrete time series, which offer promising results on both small and big data. Our starting point is the development of a rich class of Bayesian hierarchical models for variable-memory Markov chains. The particular prior structure we adopt makes it possible to design effective, linear-time algorithms that can compute most of the important features of the resulting posterior and predictive distributions without resorting to MCMC .

We have applied the resulting algorithms and methodological tools to numerous application-specific tasks (including on-line prediction, segmentation, classification, anomaly detection, entropy estimation, and causality testing) on data sets from different areas of application, including data compression, neuroscience, finance, genetics, and animal communication. Results on both simulated and real data will be presented, and brief comparisons with other approaches (including Bühlmann et al’s VLMC , Ron et al’s PST , and Raftery’s MTD ) will be discussed. 

This talk is part of the Isaac Newton Institute Seminar Series series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.


© 2006-2022, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity