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

TL;DR

This paper evaluates various time series models for forecasting call center arrivals, emphasizing the importance of modeling both mean and variance for improved accuracy and economic performance.

Contribution

It introduces a comprehensive strategy combining statistical and economic criteria for selecting call arrival forecast models, highlighting the importance of second moment modeling.

Findings

Second moment modeling improves forecast accuracy.

Seasonal Random Walk is outperformed by more complex models.

Economic evaluation favors models capturing variance.

Abstract

Call centers' managers are interested in obtaining accurate point and distributional forecasts of call arrivals in order to achieve an optimal balance between service quality and operating costs. We present a strategy for selecting forecast models of call arrivals which is based on three pillars: (i) flexibility of the 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 daily call arrivals. Although we focus mainly on point forecasts, we also analyze density forecast evaluation. We show that second moments modeling is important both for point and density forecasting and that the simple Seasonal Random Walk model is always outperformed by more general specifications. Our results suggest that call center managers should invest in the use of forecast models which describe both first and second moments of call arrivals.