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Statistical and Economic Evaluation of Time Series Models for Forecasting Arrivals at Call Centers

1st Person: Bastianin, Andrea
Additional Persons: Galeotti, Marzio; Manera, Matteo
Type of Publication: Paper
Language: English
Published: Fondazione Eni Enrico Mattei (FEEM) 2017
Series: Nota di Lavoro
Online: http://hdl.handle.net/10419/162248
id
oai_econstor.eu_10419-162248
recordtype
econstor
institution
MPG
collection
ECONSTOR
title
Statistical and Economic Evaluation of Time Series Models for Forecasting Arrivals at Call Centers
spellingShingle
Statistical and Economic Evaluation of Time Series Models for Forecasting Arrivals at Call Centers
Bastianin, Andrea
Nota di Lavoro
title_short
Statistical and Economic Evaluation of Time Series Models for Forecasting Arrivals at Call Centers
title_full
Statistical and Economic Evaluation of Time Series Models for Forecasting Arrivals at Call Centers
title_fullStr
Statistical and Economic Evaluation of Time Series Models for Forecasting Arrivals at Call Centers
title_full_unstemmed
Statistical and Economic Evaluation of Time Series Models for Forecasting Arrivals at Call Centers
title_sort
Statistical and Economic Evaluation of Time Series Models for Forecasting Arrivals at Call Centers
format
electronic Article
format_phy_str_mv
Paper
publisher
Fondazione Eni Enrico Mattei (FEEM)
publishDate
2017
language
English
author
Bastianin, Andrea
author2
Galeotti, Marzio
Manera, Matteo
author2Str
Galeotti, Marzio
Manera, Matteo
description
Call centers' managers are interested in obtaining accurate forecasts of call arrivals because these are a key input in staffing and scheduling decisions. Therefore their ability to achieve an optimal balance between service quality and operating costs ultimately hinges on forecast accuracy. We present a strategy to model selection in call centers which is based on three pillars: (i) a flexible loss function; (ii) statistical evaluation of forecast accuracy; (iii) economic evaluation of forecast performance using money metrics. We implement fourteen time series models and seven forecast combination schemes on three series of call arrivals. We show that second moment modeling is important when forecasting call arrivals. From the point of view of a call center manager, our results indicate that outsourcing the development of a forecasting model is worth its cost, since the simple Seasonal Random Walk model is always outperformed by other, relatively more sophisticated, specifications.
url
http://hdl.handle.net/10419/162248
series
Nota di Lavoro
seriesStr
Nota di Lavoro
Nota di Lavoro
series2
Nota di Lavoro
series2_facet
Nota di Lavoro
up_date
2019-04-25T02:50:14.791Z
_version_
1631752679813283842

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