A Hybrid Deep Learning Model Based on BLSTM-GRU for Enhanced Call Volume Forecasting in Call Centers
محورهای موضوعی : Machine learning
Armaghan Nikfar
1
,
Sasan Harifi
2
,
Javad Mohammadzadeh
3
1 - Department of Computer Engineering, Ka.C., Islamic Azad University, Karaj, Iran
2 - Department of Computer Engineering, Ka.C., Islamic Azad University, Karaj, Iran
3 - Department of Computer Engineering, Ka.C., Islamic Azad University, Karaj, Iran
کلید واژه: Long Short-Term Memory (LSTM), Call Center, BLSTM-GRU, Hybrid Deep Learning, Prediction,
چکیده مقاله :
This research aims to develop a forecasting model for call center call volume by incorporating characteristics unique to each country and call center environment. Using historical call data, calendar events, and other relevant input variables, the goal is to create a framework that is capable of accurately simulating and predicting the volume of incoming calls over specified time periods. For this purpose, we employ advanced recurrent neural network architectures, including MLP, RNN, LSTM, and a hybrid BLSTM-GRU model, to analyze temporal patterns in call data. The BLSTM-GRU model, in particular, uses two-way and gating processing mechanisms to more effectively capture complex temporal dependencies. The dataset consists of daily call volume over a 4-year period along with text features formatted in Excel for ease of training and evaluation. Furthermore, the forecasting framework integrates a comparison mechanism that compares the predicted call volume against the actual values to assess the accuracy and robustness of the model. Experimental results show that the BLSTM-GRU model outperforms standard architectures, achieving a forecasting accuracy of 94.12%, whereas the optimized single LSTM baseline reaches 81.35%. To increase operational efficiency, the framework also generates shift schedules that comply with call center standards and prevent call queues by recommending the required number of agents. The superior performance of the proposed BLSTM-GRU model highlights its potential application in various contact centers around the world and provides a scalable solution for intelligent contact center management.
This research aims to develop a forecasting model for call center call volume by incorporating characteristics unique to each country and call center environment. Using historical call data, calendar events, and other relevant input variables, the goal is to create a framework that is capable of accurately simulating and predicting the volume of incoming calls over specified time periods. For this purpose, we employ advanced recurrent neural network architectures, including MLP, RNN, LSTM, and a hybrid BLSTM-GRU model, to analyze temporal patterns in call data. The BLSTM-GRU model, in particular, uses two-way and gating processing mechanisms to more effectively capture complex temporal dependencies. The dataset consists of daily call volume over a 4-year period along with text features formatted in Excel for ease of training and evaluation. Furthermore, the forecasting framework integrates a comparison mechanism that compares the predicted call volume against the actual values to assess the accuracy and robustness of the model. Experimental results show that the BLSTM-GRU model outperforms standard architectures, achieving a forecasting accuracy of 94.12%, whereas the optimized single LSTM baseline reaches 81.35%. To increase operational efficiency, the framework also generates shift schedules that comply with call center standards and prevent call queues by recommending the required number of agents. The superior performance of the proposed BLSTM-GRU model highlights its potential application in various contact centers around the world and provides a scalable solution for intelligent contact center management.
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