Forecasting arrivals to a call centre using machine learning and deep learning

With the increasing complexity of call center operations, accurate call arrival prediction has become a crucial research area, attracting significant attention from both academia and industry. Forecasting call arrivals is essential for effective resource allocation, staffing decisions, and service level planning, ultimately improving operational efficiency and customer satisfaction. This study investigates the effectiveness of combining Bayesian optimization (BO) with feature selection to enhance call arrival prediction accuracy. We analyzed various machine learning (ML) and deep learning (DL) models, including Random Forest (RF), Multi-layer Perceptron (MLP), Support Vector Machine (SVM), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM). Using real-world call center data, we employed three datasets with daily, hourly, and half-hourly observations to predict call volume and Average Handling Time (AHT). Autocorrelation functions revealed patterns in these datasets. Both multivariate and univariate prediction approaches were evaluated. Using the Mean Absolute Error (MAE) metric and Root Mean Squared Error metric (RMSE), we demonstrated that optimized models with selected features consistently outperformed baseline models and optimized models with all features. Specifically, DL models, notably CNN and LSTM, showed robust responsiveness to combined BO and feature selection. ML models, especially MLP and RF, had strong baseline performances and also benefited from our approach. Depending on the dataset, performance gains from our approach ranged from 14% to 88% for ML models and 92% to 97% for DL models for both call volume and AHT predictions for multivariate models. For univariate models with only BO, percentage improvements ranged from 20% to 71% for ML models and 50% to 96.6% for DL models. These results highlight the effectiveness of combining feature selection with BO for more accurate predictions of both call volume and AHT. Keywords: Call Arrivals forecasting, machine learning, Hyperparameter Optimization, Feature Selection