公开(公告)号：US20240412131A1

公开(公告)日：2024-12-12

申请号：US18207344

申请日：2023-06-08

Applicant: GENESYS CLOUD SERVICES, INC.

Inventor： BAYU AJI WICAKSONO ,  WEI XUN TER ,  CHRISTOPHER E. JOHNSON ,  DANIEL J. CHAPDELAINE ,  CHARLES DAVID FICO ,  VIDIT MEHTA

IPC: G06Q10/0631 ,  G06Q10/04

Abstract: A method for selecting forecasting models for generating timeseries workload forecasts for a contact center covering varying timeseries granularities and operating horizons. The method includes selecting, via a first selection process, a first select forecasting model from first candidate forecasting models for forecasting a workload level in accordance with a lower-granularity timeseries. The first selection process may include the steps of: receiving a first timeseries dataset; defining different timeseries datasets within the first timeseries dataset, including a first shorterm dataset and first longterm dataset; testing, using the first longterm dataset, each first candidate forecasting model in accordance with a first longterm cross-validation process; testing, using the first shorterm dataset, each first candidate forecasting model in accordance with a first shorterm cross-validation process; and calculating, for each of the first candidate forecasting models, an combined accuracy score based on the testing.

公开(公告)号：US20240412130A1

公开(公告)日：2024-12-12

申请号：US18207311

申请日：2023-06-08

Applicant: GENESYS CLOUD SERVICES, INC.

Inventor： BAYU AJI WICAKSONO ,  WEI XUN TER ,  CHRISTOPHER E. JOHNSON ,  DANIEL J. CHAPDELAINE ,  CHARLES DAVID FICO ,  VIDIT MEHTA

IPC: G06Q10/0631 ,  G06Q10/0637 ,  G06Q10/1053

Abstract: A method for determining a select forecasting model from among candidate forecasting models for improving workload forecasts generated by a single forecasting model that cover both longterm and shorterm operating horizons. The method may include: receiving a timeseries dataset having values associated with operational metrics of a contact center; receiving the candidate forecasting models, each of the candidate forecasting models configured to receive values of the input operational metrics and calculate therefrom a forecasted value for a value of a target operational metric; using the timeseries dataset to test each of the candidate forecasting models in accordance with a cross-validation process; and selecting the select forecasting model from among the candidate forecasting models based on comparing accuracy scores calculated for the candidate forecasting models as part of the cross-validation process.