Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi

Yazarlar: Mohamed BALLOUCH, Fatih AKAY, Sevtap ERDEM, Mesut TARTUK, Taha Furkan NURDAĞ, Hasan Hüseyin YURDAGÜL

Konular:Bilgisayar Bilimleri, Bilgi Sistemleri

DOI:10.47495/okufbed.824870

Anahtar Kelimeler:Machine Learning,Call Center,Forecasting,Time Lag

Özet: A call center is an office equipped to handle a large volume of telephone calls for an organization, for which the ability to forecast calls is a key factor. By forecasting the number of calls accurately, a company can plan staffing needs, meet service level requirements, improve customer satisfaction and benefit from many other optimizations. In this paper, we develop Multilayer Perceptron (MLP) and Long-Short Term Memory (LSTM) based models combined with time lags to forecast the number of call arrivals in a call center. We forecast 12, 24, 36 and 48 values ahead and the performance of the forecasting models has been evaluated using the Mean Absolute Error (MAE). The MLP based model results show that the MAE values change between 1,50 and 13,58 and LSTM based model results show that the MAE values change between 19,99 and 66,74.

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