Effective Bayesian Modeling of Groups of Related Count Time Series

Nicolas Chapados
Proceedings of the 31st International Conference on Machine Learning, PMLR 32(2):1395-1403, 2014.

Abstract

Time series of counts arise in a variety of forecasting applications, for which traditional models are generally inappropriate. This paper introduces a hierarchical Bayesian formulation applicable to count time series that can easily account for explanatory variables and share statistical strength across groups of related time series. We derive an efficient approximate inference technique, and illustrate its performance on a number of datasets from supply chain planning.

Cite this Paper

BibTeX
@InProceedings{pmlr-v32-chapados14, title = {Effective Bayesian Modeling of Groups of Related Count Time Series}, author = {Chapados, Nicolas}, booktitle = {Proceedings of the 31st International Conference on Machine Learning}, pages = {1395--1403}, year = {2014}, editor = {Xing, Eric P. and Jebara, Tony}, volume = {32}, number = {2}, series = {Proceedings of Machine Learning Research}, address = {Bejing, China}, month = {22--24 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v32/chapados14.pdf}, url = {https://proceedings.mlr.press/v32/chapados14.html}, abstract = {Time series of counts arise in a variety of forecasting applications, for which traditional models are generally inappropriate. This paper introduces a hierarchical Bayesian formulation applicable to count time series that can easily account for explanatory variables and share statistical strength across groups of related time series. We derive an efficient approximate inference technique, and illustrate its performance on a number of datasets from supply chain planning.} }
Endnote
%0 Conference Paper %T Effective Bayesian Modeling of Groups of Related Count Time Series %A Nicolas Chapados %B Proceedings of the 31st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2014 %E Eric P. Xing %E Tony Jebara %F pmlr-v32-chapados14 %I PMLR %P 1395--1403 %U https://proceedings.mlr.press/v32/chapados14.html %V 32 %N 2 %X Time series of counts arise in a variety of forecasting applications, for which traditional models are generally inappropriate. This paper introduces a hierarchical Bayesian formulation applicable to count time series that can easily account for explanatory variables and share statistical strength across groups of related time series. We derive an efficient approximate inference technique, and illustrate its performance on a number of datasets from supply chain planning.
RIS
TY - CPAPER TI - Effective Bayesian Modeling of Groups of Related Count Time Series AU - Nicolas Chapados BT - Proceedings of the 31st International Conference on Machine Learning DA - 2014/06/18 ED - Eric P. Xing ED - Tony Jebara ID - pmlr-v32-chapados14 PB - PMLR DP - Proceedings of Machine Learning Research VL - 32 IS - 2 SP - 1395 EP - 1403 L1 - http://proceedings.mlr.press/v32/chapados14.pdf UR - https://proceedings.mlr.press/v32/chapados14.html AB - Time series of counts arise in a variety of forecasting applications, for which traditional models are generally inappropriate. This paper introduces a hierarchical Bayesian formulation applicable to count time series that can easily account for explanatory variables and share statistical strength across groups of related time series. We derive an efficient approximate inference technique, and illustrate its performance on a number of datasets from supply chain planning. ER -
APA
Chapados, N.. (2014). Effective Bayesian Modeling of Groups of Related Count Time Series. Proceedings of the 31st International Conference on Machine Learning, in Proceedings of Machine Learning Research 32(2):1395-1403 Available from https://proceedings.mlr.press/v32/chapados14.html.

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