Speaker
Mr
Bruno Flores
(ICMAT-CSIC)
Description
Large-scale dynamic forecasting of non-negative count series is a major challenge in many areas like epidemic monitoring or retail management. We propose Bayesian state-space models that are flexible enough to adequately forecast high and low count series and exploit cross-series relationships with a multivariate approach. This is illustrated with a large scale sales forecasting problem faced by a major retail company, integrated within its inventory management planning methodology. The company has hundreds of shops in several countries, each one with thousands of references.
| Keywords | Count time series; Sales forecasting; Dynamic generalized linear models. |
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Primary author
Mr
Bruno Flores
(ICMAT-CSIC)