Forecasting call center arrivals using temporal memory networks and gradient boosting algorithm

For any call center facility, the number of call arrivals represents a key component between customer satisfaction and budget constraints. Hence, the ability to accurately forecast the number of calls for a particular period of time is an effective measure in planning customer service and reducing a...

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Vydáno v:	Expert systems with applications Ročník 224; s. 119983
Hlavní autoři:	Chacón, Henry, Koppisetti, Vishwa, Hardage, David, Choo, Kim-Kwang Raymond, Rad, Paul
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 15.08.2023
Témata:	Call arrivals Gradient boosting methods GRU Holt–Winters Inhomogeneous Poisson process LSTM Normal copulas Recurrent neural networks SARIMA models Time dependency Time series forecasting Call arrivals Recurrent neural networks LSTM Holt–Winters Inhomogeneous Poisson process Time dependency Time series forecasting GRU SARIMA models Gradient boosting methods Normal copulas
ISSN:	0957-4174, 1873-6793
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Popis
Shrnutí:	For any call center facility, the number of call arrivals represents a key component between customer satisfaction and budget constraints. Hence, the ability to accurately forecast the number of calls for a particular period of time is an effective measure in planning customer service and reducing any resource waste. In this research, presented as a comparative study in a tutorial style, we present call arrival forecasting comparison between classical time series methods such as Holt–Winters, exponential smoothing, and SARIMA with machine/deep learning techniques like Recurrent Neural Networks and gradient boosting approach. To test the models, we use real-life call logs from a national US insurance company collected for ten months. The series exhibits an inhomogeneous Poisson process and dependencies between aggregated periods. Call center managers commonly require forecast results on short and long-term periods to determine the required headcount based on expected service quality. In our case, we used half-hourly and daily aggregations for short and long-term forecasts. Because the data used is less than a year long, we provided enough seasonal periods in the short-term period. In this case, both deep learning models reported minimum error followed by the boosting approach. This is not the case for long-term periods, where the provided series is less than a year. The boosting approach reports better error results than any of the models used, even deep learning models which report the worst errors from the model’s list. Those results indicate that on limited seasonality periods, deep learning models are incapable of generalizing different volatility fluctuations compared to boosting and classical approaches. Considering boosting errors on short-term periods are comparable to deep learning models, we suggest the use of boosting as a benchmark approach to forecast call arrivals for the inhomogeneous Poisson process, specifically on limited seasonal periods. •Evaluation of machine learning methods with conventional call arrival approaches.•Identification of relevant time components to forecast call arrivals.•Forecasting call center arrivals using temporal memory networks & gradient boosting.
ISSN:	0957-4174 1873-6793
DOI:	10.1016/j.eswa.2023.119983