Documents disponibles écrits par cet auteur

Density forecasting of intraday call center arrivals using models based on exponential smoothing / James W. Taylor in Management science, Vol. 58 N° 3 (Mars 2012) 

Public ISBD [article]  in Management science > Vol. 58 N° 3 (Mars 2012) . - pp. 534-549 Titre : Density forecasting of intraday call center arrivals using models based on exponential smoothing Type de document : texte imprimé Auteurs : James W. Taylor, Auteur Année de publication : 2012 Article en page(s) : pp. 534-549 Note générale : Management Langues : Anglais (eng) Mots-clés : Call  centers  Arrival  rate  Density  forecasting  Exponential  smoothing  Seasonality Résumé : A key input to the call center staffing process is a forecast for the number of calls arriving. Density forecasts of arrival rates are needed for analytical call center models, which assume Poisson arrivals with a stochastic arrival rate. Density forecasts of call volumes can be used in simulation models and are also important for the analysis of outsourcing contracts. A forecasting method, which has previously shown strong potential, is Holt–Winters exponential smoothing adapted for modeling the intraday and intraweek cycles in intraday data. To enable density forecasting of the arrival volume and rate, we develop a Poisson count model, with gamma distributed arrival rate, which captures the essential features of this exponential smoothing method. The apparent stationary level in our data leads us to develop versions of the new model for series with stationary levels. We evaluate forecast accuracy up to two weeks ahead using data from three organizations. We find that the stationary level models improve prediction beyond approximately two days ahead, and that these models perform well in comparison with sophisticated benchmarks. This is confirmed by the results of a call center simulation model, which demonstrates the use of arrival rate density forecasting to support staffing decisions. DEWEY : 658 ISSN : 0025-1909 En ligne : http://mansci.journal.informs.org/content/58/3.toc [article] Density forecasting of intraday call center arrivals using models based on exponential smoothing [texte imprimé] / James W. Taylor, Auteur . - 2012 . - pp. 534-549. Management Langues : Anglais (eng) in Management science > Vol. 58 N° 3 (Mars 2012) . - pp. 534-549 Mots-clés : Call  centers  Arrival  rate  Density  forecasting  Exponential  smoothing  Seasonality Résumé : A key input to the call center staffing process is a forecast for the number of calls arriving. Density forecasts of arrival rates are needed for analytical call center models, which assume Poisson arrivals with a stochastic arrival rate. Density forecasts of call volumes can be used in simulation models and are also important for the analysis of outsourcing contracts. A forecasting method, which has previously shown strong potential, is Holt–Winters exponential smoothing adapted for modeling the intraday and intraweek cycles in intraday data. To enable density forecasting of the arrival volume and rate, we develop a Poisson count model, with gamma distributed arrival rate, which captures the essential features of this exponential smoothing method. The apparent stationary level in our data leads us to develop versions of the new model for series with stationary levels. We evaluate forecast accuracy up to two weeks ahead using data from three organizations. We find that the stationary level models improve prediction beyond approximately two days ahead, and that these models perform well in comparison with sophisticated benchmarks. This is confirmed by the results of a call center simulation model, which demonstrates the use of arrival rate density forecasting to support staffing decisions. DEWEY : 658 ISSN : 0025-1909 En ligne : http://mansci.journal.informs.org/content/58/3.toc

Wind power density forecasting using ensemble predictions and time series models / James W. Taylor in IEEE transactions on energy conversion, Vol. 24 N° 3 (Septembre 2009) 

Public ISBD [article]  in IEEE transactions on energy conversion > Vol. 24 N° 3 (Septembre 2009) . - pp. 775 - 782 Titre : Wind power density forecasting using ensemble predictions and time series models Type de document : texte imprimé Auteurs : James W. Taylor, Auteur ; McSharry, Patrick E., Auteur ; Buizza, Roberto, Auteur Année de publication : 2010 Article en page(s) : pp. 775 - 782 Note générale : energy conversion Langues : Anglais (eng) Mots-clés : Time  series;  wind  power Résumé : Wind power is an increasingly used form of renewable energy. The uncertainty in wind generation is very large due to the inherent variability in wind speed, and this needs to be understood by operators of power systems and wind farms. To assist with the management of this risk, this paper investigates methods for predicting the probability density function of generated wind power from one to ten days ahead at five U.K. wind farm locations. These density forecasts provide a description of the expected future value and the associated uncertainty. We construct density forecasts from weather ensemble predictions, which are a relatively new type of weather forecast generated from atmospheric models. We also consider density forecasting from statistical time series models. The best results for wind power density prediction and point forecasting were produced by an approach that involves calibration and smoothing of the ensemble-based wind power density. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5224014&sortType%3Das [...] [article] Wind power density forecasting using ensemble predictions and time series models [texte imprimé] / James W. Taylor, Auteur ; McSharry, Patrick E., Auteur ; Buizza, Roberto, Auteur . - 2010 . - pp. 775 - 782. energy conversion Langues : Anglais (eng) in IEEE transactions on energy conversion > Vol. 24 N° 3 (Septembre 2009) . - pp. 775 - 782 Mots-clés : Time  series;  wind  power Résumé : Wind power is an increasingly used form of renewable energy. The uncertainty in wind generation is very large due to the inherent variability in wind speed, and this needs to be understood by operators of power systems and wind farms. To assist with the management of this risk, this paper investigates methods for predicting the probability density function of generated wind power from one to ten days ahead at five U.K. wind farm locations. These density forecasts provide a description of the expected future value and the associated uncertainty. We construct density forecasts from weather ensemble predictions, which are a relatively new type of weather forecast generated from atmospheric models. We also consider density forecasting from statistical time series models. The best results for wind power density prediction and point forecasting were produced by an approach that involves calibration and smoothing of the ensemble-based wind power density. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5224014&sortType%3Das [...]