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

Kyung Hyun Cho cho.k.hyun at gmail.com
Tue Nov 11 20:58:58 EST 2014


Dear all,

This is a reminder about the teatalk tomorrow! Nicolas will tell us about
this recent work presented at ICML 2014.

Hope to see many of you there!
- Cho

On Thu, Nov 6, 2014 at 2:44 PM, Kyung Hyun Cho <cho.k.hyun at gmail.com> wrote:

> 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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