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

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- 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.