Forecasting call center arrivals using temporal memory networks and gradient boosting algorithm

For any call center facility, the number of call arrivals represents a key component between customer satisfaction and budget constraints. Hence, the ability to accurately forecast the number of calls for a particular period of time is an effective measure in planning customer service and reducing a...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Expert systems with applications Ročník 224; s. 119983
Hlavní autoři:	Chacón, Henry, Koppisetti, Vishwa, Hardage, David, Choo, Kim-Kwang Raymond, Rad, Paul
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 15.08.2023
Témata:	Call arrivals Gradient boosting methods GRU Holt–Winters Inhomogeneous Poisson process LSTM Normal copulas Recurrent neural networks SARIMA models Time dependency Time series forecasting Call arrivals Recurrent neural networks LSTM Holt–Winters Inhomogeneous Poisson process Time dependency Time series forecasting GRU SARIMA models Gradient boosting methods Normal copulas
ISSN:	0957-4174, 1873-6793
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!