Call Arrival Forecasting Using Feature Selection and Bayesian Optimization in Machine Learning and Deep Learning Models

Rukia Nakkazi; Mehmet Fatih Akay

Article Details

Call Arrival Forecasting Using Feature Selection and Bayesian Optimization in Machine Learning and Deep Learning Models

Authors:

Rukia Nakkazi | Mehmet Fatih Akay

Publisher:

Cukurova University

Main Topics:

Software Engineering (Other)

Journal:

Cukurova University Journal of Natural and Applied Sciences Vol.4 Issue 1, pp.1-14

Document Type:

Article

Publication Date:

6/2025

Document Language:

English

Document ID (Org No.):

2001352108

pISSN:

2822-2938

Keywords:

Abstract:

 With the increasing complexity and dynamic nature of call center operations, accurate call arrival prediction has emerged as a significant research area, attracting attention from both academia and industry. Forecasting call arrivals is essential for effective resource allocation, staffing decisions, and service level planning, ultimately contributing to improved operational efficiency and customer satisfaction. study investigates the effectiveness of combined feature selection and Bayesian optimization techniques in enhancing call arrival prediction accuracy. We conducted a comprehensive analysis of several ML and DL models, our experiments compared model performance using a combined feature selection and model optimization strategy. Utilizing real-world data from call center operations, we employed three datasets with daily, hourly, and half-hourly observations to predict call volume and Average Handling Time AHT. Our findings revealed a significant enhancement in model efficiency and predictive accuracy with the integration of Bayesian optimization. Specifically, for call volume predictions, the most dramatic decrease in Mean Absolute Error MAE was observed with hourly intervals, where the CNN model with selected features achieved a reduction from 0.74 to 0.02, marking a 97.3 error reduction. For Average Handle Time AHT, the most notable improvement was seen with half-hourly aggregations, where the CNN model with selected features showed a reduction from 0.78 to 0.02, resulting in a 97.44 error reduction. These results highlight the effectiveness of combining feature selection with Bayesian optimization methods for more accurate predictions of both call volume and AHT. Feature selection not only improved the performance of BO-optimized models but also provided valuable insights into the most relevant features for different predictive tasks. Furthermore, our findings emphasize the importance of using shorter time intervals, such as hourly and half-hourly aggregations, for improved prediction accuracy. This dual approach emphasizes the potential of advanced optimization and feature selection techniques in enhancing predictive modeling accuracy. These findings are important for businesses that depend on efficient call center operations, as they demonstrate the advantages of using advanced optimization techniques and sophisticated modeling methods.

 With the increasing complexity and dynamic nature of call center operations, accurate call arrival prediction has emerged as a significant research area, attracting attention from both academia and industry. Forecasting call arrivals is essential for effective resource allocation, staffing decisions, and service level planning, ultimately contributing to improved operational efficiency and customer satisfaction. study investigates the effectiveness of combined feature selection and Bayesian optimization techniques in enhancing call arrival prediction accuracy. We conducted a comprehensive analysis of several ML and DL models, our experiments compared model performance using a combined feature selection and model optimization strategy. Utilizing real-world data from call center operations, we employed three datasets with daily, hourly, and half-hourly observations to predict call volume and Average Handling Time AHT. Our findings revealed a significant enhancement in model efficiency and predictive accuracy with the integration of Bayesian optimization. Specifically, for call volume predictions, the most dramatic decrease in Mean Absolute Error MAE was observed with hourly intervals, where the CNN model with selected features achieved a reduction from 0.74 to 0.02, marking a 97.3 error reduction. For Average Handle Time AHT, the most notable improvement was seen with half-hourly aggregations, where the CNN model with selected features showed a reduction from 0.78 to 0.02, resulting in a 97.44 error reduction. These results highlight the effectiveness of combining feature selection with Bayesian optimization methods for more accurate predictions of both call volume and AHT. Feature selection not only improved the performance of BO-optimized models but also provided valuable insights into the most relevant features for different predictive tasks. Furthermore, our findings emphasize the importance of using shorter time intervals, such as hourly and half-hourly aggregations, for improved prediction accuracy. This dual approach emphasizes the potential of advanced optimization and feature selection techniques in enhancing predictive modeling accuracy. These findings are important for businesses that depend on efficient call center operations, as they demonstrate the advantages of using advanced optimization techniques and sophisticated modeling methods.

 With the increasing complexity and dynamic nature of call center operations, accurate call arrival prediction has emerged as a significant research area, attracting attention from both academia and industry. Forecasting call arrivals is essential for effective resource allocation, staffing decisions, and service level planning, ultimately contributing to improved operational efficiency and customer satisfaction. study investigates the effectiveness of combined feature selection and Bayesian optimization techniques in enhancing call arrival prediction accuracy. We conducted a comprehensive analysis of several ML and DL models, our experiments compared model performance using a combined feature selection and model optimization strategy. Utilizing real-world data from call center operations, we employed three datasets with daily, hourly, and half-hourly observations to predict call volume and Average Handling Time AHT. Our findings revealed a significant enhancement in model efficiency and predictive accuracy with the integration of Bayesian optimization. Specifically, for call volume predictions, the most dramatic decrease in Mean Absolute Error MAE was observed with hourly intervals, where the CNN model with selected features achieved a reduction from 0.74 to 0.02, marking a 97.3 error reduction. For Average Handle Time AHT, the most notable improvement was seen with half-hourly aggregations, where the CNN model with selected features showed a reduction from 0.78 to 0.02, resulting in a 97.44 error reduction. These results highlight the effectiveness of combining feature selection with Bayesian optimization methods for more accurate predictions of both call volume and AHT. Feature selection not only improved the performance of BO-optimized models but also provided valuable insights into the most relevant features for different predictive tasks. Furthermore, our findings emphasize the importance of using shorter time intervals, such as hourly and half-hourly aggregations, for improved prediction accuracy. This dual approach emphasizes the potential of advanced optimization and feature selection techniques in enhancing predictive modeling accuracy. These findings are important for businesses that depend on efficient call center operations, as they demonstrate the advantages of using advanced optimization techniques and sophisticated modeling methods.

 With the increasing complexity and dynamic nature of call center operations, accurate call arrival prediction has emerged as a significant research area, attracting attention from both academia and industry. Forecasting call arrivals is essential for effective resource allocation, staffing decisions, and service level planning, ultimately contributing to improved operational efficiency and customer satisfaction. study investigates the effectiveness of combined feature selection and Bayesian optimization techniques in enhancing call arrival prediction accuracy. We conducted a comprehensive analysis of several ML and DL models, our experiments compared model performance using a combined feature selection and model optimization strategy. Utilizing real-world data from call center operations, we employed three datasets with daily, hourly, and half-hourly observations to predict call volume and Average Handling Time AHT. Our findings revealed a significant enhancement in model efficiency and predictive accuracy with the integration of Bayesian optimization. Specifically, for call volume predictions, the most dramatic decrease in Mean Absolute Error MAE was observed with hourly intervals, where the CNN model with selected features achieved a reduction from 0.74 to 0.02, marking a 97.3 error reduction. For Average Handle Time AHT, the most notable improvement was seen with half-hourly aggregations, where the CNN model with selected features showed a reduction from 0.78 to 0.02, resulting in a 97.44 error reduction. These results highlight the effectiveness of combining feature selection with Bayesian optimization methods for more accurate predictions of both call volume and AHT. Feature selection not only improved the performance of BO-optimized models but also provided valuable insights into the most relevant features for different predictive tasks. Furthermore, our findings emphasize the importance of using shorter time intervals, such as hourly and half-hourly aggregations, for improved prediction accuracy. This dual approach emphasizes the potential of advanced optimization and feature selection techniques in enhancing predictive modeling accuracy. These findings are important for businesses that depend on efficient call center operations, as they demonstrate the advantages of using advanced optimization techniques and sophisticated modeling methods.

Article Details

Call Arrival Forecasting Using Feature Selection and Bayesian Optimization in Machine Learning and Deep Learning Models

Authors:

Rukia Nakkazi | Mehmet Fatih Akay

Publisher:

Cukurova University

Main Topics:

Software Engineering (Other)

Journal:

Cukurova University Journal of Natural and Applied Sciences Vol.4 Issue 1, pp.1-14

Document Type:

Article

Publication Date:

6/2025

Document Language:

English

Document ID (Org No.):

2001352108

pISSN:

2822-2938

Forecasting

Call Arrivals

Bayesian Optimization

Feature Selection

Issues

Article Details

Call Arrival Forecasting Using Feature Selection and Bayesian Optimization in Machine Learning and Deep Learning Models

Authors: Rukia Nakkazi | Mehmet Fatih Akay

Publisher: Cukurova University

Main Topics: Software Engineering (Other)

Journal: Cukurova University Journal of Natural and Applied Sciences Vol.4 Issue 1, pp.1-14

Document Type: Article

Publication Date: 6/2025

Document Language: English

Document ID (Org No.): 2001352108

pISSN: 2822-2938

Forecasting

Call Arrivals

Bayesian Optimization

Feature Selection

Issues

Authors:

Rukia Nakkazi | Mehmet Fatih Akay

Publisher:

Cukurova University

Main Topics:

Software Engineering (Other)

Journal:

Cukurova University Journal of Natural and Applied Sciences Vol.4 Issue 1, pp.1-14

Document Type:

Article

Publication Date:

6/2025

Document Language:

English

Document ID (Org No.):

2001352108

pISSN:

2822-2938