Please use this identifier to cite or link to this item: http://ir-ithesis.swu.ac.th/dspace/handle/123456789/2577
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dc.contributorPEARAWIT SURASAIen
dc.contributorพีรวิทญ์ สุระสายth
dc.contributor.advisorVera Sa-Ingen
dc.contributor.advisorวีระ สอิ้งth
dc.contributor.otherSrinakharinwirot Universityen
dc.date.accessioned2024-01-15T01:16:01Z-
dc.date.available2024-01-15T01:16:01Z-
dc.date.created2023
dc.date.issued15/12/2023
dc.identifier.urihttp://ir-ithesis.swu.ac.th/dspace/handle/123456789/2577-
dc.description.abstractThis study focuses on enhancing workforce management in the Citizen Service Request (CSR) Call Center dataset of the government of Cincinnati, Ohio, by improving the accuracy of call arrival forecasts. Recognizing the pivotal role of precise call arrival predictions in optimizing call center operations, this the study conducts experiments by utilizing a range of forecasting models, including statistical, machine learning, and neural network approaches. Feature engineering was proposed to broaden the scope of features for forecasting. The top-performing models are evaluated based on key metrics such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and R-Squared (R²) forecasting performance. The experimental results highlighted the comparative performance of various models, such as SARIMAX, Light Gradient Boosting Machine (Light GBM), Gradient Boosting Regressor (GBR), eXtreme Gradient Boosting (XGBoost), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Among these, Support Vector Regression (SVR) leads in accuracy with an MAE of 25.13, an MAPE of 6.15%, an RMSE of 34.46, and an R² of 90.56%. The features of abandon rate, answer speed, service level calls, and the 1st and 5th lags, were identified as the most importance feature in this research. These findings provide valuable insights for the improvement of workforce management strategies in call center operations, emphasizing the effectiveness of machine learning algorithms in achieving more accurate call arrival forecasts.en
dc.description.abstract-th
dc.language.isoen
dc.publisherSrinakharinwirot University
dc.rightsSrinakharinwirot University
dc.subjectWorkforce Manangenten
dc.subjectMachine Learningen
dc.subjectSupport Vector Machineen
dc.subject.classificationDecision Sciencesen
dc.subject.classificationInformation and communicationen
dc.subject.classificationStatisticsen
dc.titleTIME SERIES FORECAST OF CALL ARRIVALS USING MACHINE LEARNING METHODSen
dc.titleการคาดการณ์จำนวนสายที่ติดต่อโดยใช้แบบจำลองการเรียนรู้ของเครื่องด้วยข้อมูลเชิงอนุกรมเวลาth
dc.typeMaster’s Projecten
dc.typeสารนิพนธ์th
dc.contributor.coadvisorVera Sa-Ingen
dc.contributor.coadvisorวีระ สอิ้งth
dc.contributor.emailadvisorvera@swu.ac.th
dc.contributor.emailcoadvisorvera@swu.ac.th
dc.description.degreenameMASTER OF SCIENCE (M.Sc.)en
dc.description.degreenameวิทยาศาสตรมหาบัณฑิต (วท.ม.)th
dc.description.degreelevel-en
dc.description.degreelevel-th
dc.description.degreedisciplineDepartment of Computer Scienceen
dc.description.degreedisciplineภาควิชาวิทยาการคอมพิวเตอร์th
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