D. Barrow, N. Kourentzes, 2015, 27th European Conference on operational Research, Glasgow.
A key challenge for call centres remains the forecasting of high frequency call arrivals collected in hourly or shorter time buckets. These forecasts are required for decisions concerning the scheduling, hiring and training of staff. In addition to the high frequency nature of call arrival series and the complex seasonal patterns, including the multiple seasonal cycles, call arrival data often contain a large number of anomalies, driven by holidays, special events, promotional activities and system failures. This study presents an approach based on artificial neural networks (ANNs) for forecasting intraday call arrivals. In so doing, we empirically evaluate alternative methodologies for modelling and forecasting outliers in high frequency data, which span over several periods, addressing a gap in research of practical significance considering the difficulty and the cost associated with manual exploration and treatment of such data. We assess the performance of different ANN modelling methodologies in terms of the accuracy with which normal and outlying periods are modelled. Multi-period outliers are modelled using alternative encodings ranging from binary dummy variables to functional profiles, as well as segmenting the series to separate it into outlying and normal days. Results show that ANNs outperform conventional benchmarks and are capable of modelling high frequency outliers using relatively simple outlier modelling approaches.