公开(公告)号：US12177088B2

公开(公告)日：2024-12-24

申请号：US17938742

申请日：2022-10-07

Applicant: Adobe Inc.

Inventor： Wei Zhang ,  Ilya Borisovich Reznik

IPC: H04L41/149 ,  H04L41/14 ,  H04L41/147

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that determine internet traffic data loss from internet traffic data including bulk ingested data utilizing an internet traffic forecasting model. In particular, the disclosed systems detect that observed internet traffic data includes bulk ingested internet traffic data. In addition, the disclosed systems determine a predicted traffic volume for an outage period from the bulk ingested internet traffic data utilizing an internet traffic forecasting model. The disclosed systems further generate a decomposed predicted traffic volume for the outage period. The disclosed systems also determine an internet traffic data loss for the outage period from the decomposed predicted traffic volume while calibrating for pattern changes and late data from previous periods.

公开(公告)号：US20240119831A1

公开(公告)日：2024-04-11

申请号：US17938742

申请日：2022-10-07

Applicant: Adobe Inc.

Inventor： Wei Zhang ,  Ilya Borisovich Reznik

IPC: G08G1/01

CPC classification number: G08G1/0145 ,  G08G1/0133 ,  G08G1/0141

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that determine internet traffic data loss from internet traffic data including bulk ingested data utilizing an internet traffic forecasting model. In particular, the disclosed systems detect that observed internet traffic data includes bulk ingested internet traffic data. In addition, the disclosed systems determine a predicted traffic volume for an outage period from the bulk ingested internet traffic data utilizing an internet traffic forecasting model. The disclosed systems further generate a decomposed predicted traffic volume for the outage period. The disclosed systems also determine an internet traffic data loss for the outage period from the decomposed predicted traffic volume while calibrating for pattern changes and late data from previous periods.