[Lisa_teatalk] Tea Talk 12 Nov Wed @15.00 AA3195 by Nicolas Chapados

Kyung Hyun Cho cho.k.hyun at gmail.com
Thu Nov 6 14:44:48 EST 2014


Dear all,

Nicolas Chapados (ApSTAT Technology, Ph.D. from LISA Lab) will tell us
about his latest work on modelling the time series of integer counts, which
was presented at ICML 2014 this year. The talk will be held at the usual
place AA3195, but will *start at 15.00*.

Hope to see many of you at the talk!
- Cho

===
- Speaker: Dr. Nicolas Chapados, ApSTAT Technology
- Date/Time: 12 Nov (Wed) 15.00 - 16.00
- Place: AA3195
- Title: Effective Bayesian Modeling of Groups of Related Count Time Series
- Abstract:
Time series of integer count data arise in a wide variety of practical
situations: the number of clicks on an URL in a given time span, the
time-dependent consumer demand for pink T-shirts at a fashion store, or the
number of spare parts needed to keep a fleet of fighter jets in working
order on an aircraft carrier. When these counts are either small, highly
variable or contain a large number of zero values — the signature of rare
events — classical time series models are grossly misspecified and often
yield unreasonable forecast distributions. In this talk, I describe a
hierarchical probabilistic state-space model for count data, for which
approximate Bayesian inference is simple and fast. The model can handle
arbitrary explanatory variables and recognizes the common scenario of “weak
coupling” between a group of related time series (e.g. the demand for a
seasonal product at several stores of the same chain should share similar
seasonal patterns without necessarily exhibiting strong
cross-correlations). I illustrate the forecasting performance on a number
of benchmarks from supply chain planning, although the range of
applicability of the approach is much wider. I also show that the model can
extrapolate useful seasonalities from extremely short time series (only 4
observations!) by automatically borrowing statistical strength from related
longer series. This work was presented at the ICML’14 conference in
Beijing, last June.
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