公开(公告)号：US11537874B1

公开(公告)日：2022-12-27

申请号：US16101118

申请日：2018-08-10

Applicant: Amazon Technologies, Inc.

Inventor： Yuyang Wang ,  Alexander Johannes Smola ,  Dean P. Foster ,  Tim Januschowski

IPC: G06N3/08 ,  G06N7/00 ,  G06K9/62 ,  G06Q10/04 ,  G06N20/00

Abstract: Techniques for forecasting using deep factor models with random effects are described. A forecasting framework combines the strengths of both classical and neural forecasting methods in a global-local framework for forecasting multiple time series. A global model captures the common latent patterns shared by all time series, while a local model explains the variations at the individual level.

公开(公告)号：US11675646B2

公开(公告)日：2023-06-13

申请号：US16912312

申请日：2020-06-25

Applicant: Amazon Technologies, Inc.

Inventor： Jan Gasthaus ,  Mohamed El Fadhel Ayed ,  Lorenzo Stella ,  Tim Januschowski

IPC: G06F11/07 ,  G06F16/23 ,  G06F40/20 ,  G06F11/22

CPC classification number: G06F11/079 ,  G06F11/0793 ,  G06F11/2263 ,  G06F16/2379 ,  G06F40/20

Abstract: Techniques for anomaly detection are described. An exemplary method includes receiving a request to monitor for anomalies from one or more data sources; analyzing time-series data from the one or more data sources; generating a recommendation for handling the determined anomaly, the recommendation generated by performing one or more of a root cause analysis, a heuristic analysis, and an incident similarity analysis; and reporting the anomaly and recommendation.

公开(公告)号：US11281969B1

公开(公告)日：2022-03-22

申请号：US16116631

申请日：2018-08-29

Applicant: Amazon Technologies, Inc.

Inventor： Syama Rangapuram ,  Jan Alexander Gasthaus ,  Tim Januschowski ,  Matthias Seeger ,  Lorenzo Stella

IPC: G06N3/08 ,  G06N7/00 ,  G06Q10/04 ,  G06F17/13 ,  G06K9/62

Abstract: A composite time series forecasting model comprising a neural network sub-model and one or more state space sub-models corresponding to individual time series is trained. During training, output of the neural network sub-model is used to determine parameters of the state space sub-models, and a loss function is computed using the values of the time series and probabilistic values generated as output by the state space sub-models. A trained version of the composite model is stored.

公开(公告)号：US12033048B1

公开(公告)日：2024-07-09

申请号：US17107820

申请日：2020-11-30

Applicant: Amazon Technologies, Inc.

Inventor： Laurent Callot ,  Jasmeet Chhabra ,  Lifan Chen ,  Ming Chen ,  Tim Januschowski ,  Andrey Kan ,  Luyang Kong ,  Baris Kurt ,  Pramuditha Perera ,  Mostafa Rahmani ,  Parminder Bhatia

IPC: H04L29/06 ,  G06F18/214 ,  G06N20/20

CPC classification number: G06N20/20 ,  G06F18/214

Abstract: Techniques for performing anomaly detection are described. An exemplary method includes receiving a request to detect potential anomalies using an anomaly detection system having at least one anomaly scoring model; processing the received data using the anomaly detection system to score the data to determine when the data is potentially anomalous based on one or more thresholds; requesting feedback of at least one determined potential anomaly; receiving feedback on the least one determined potential anomaly; and adjusting at least one of one or more of thresholds used to determine potential anomalies and what is considered an anomaly without adjusting the at least one anomaly scoring model.

公开(公告)号：US20230004564A1

公开(公告)日：2023-01-05

申请号：US17364808

申请日：2021-06-30

Applicant: Amazon Technologies, Inc.

Inventor： Steffen Rochel ,  Tim Januschowski ,  Sainath Chowdary Mallidi ,  Andrew Edward Caldwell ,  Islam Mohamed Hatem A Atta ,  Valentin Flunkert ,  Arjun Ashok

IPC: G06F16/2455 ,  G06F16/2453 ,  G06F16/28

Abstract: Placement decisions may be made to place data in a multi-tenant cache. Usage of multi-tenant cache nodes for performing access requests may be obtained. Usage prediction techniques may be applied to the usage to determine placement decisions for data amongst the multi-tenant cache nodes. Placement actions for the data amongst at the multi-tenant cache nodes may be performed according to the placement decisions.

公开(公告)号：US11829364B2

公开(公告)日：2023-11-28

申请号：US17364808

申请日：2021-06-30

Applicant: Amazon Technologies, Inc.

Inventor： Steffen Rochel ,  Tim Januschowski ,  Sainath Chowdary Mallidi ,  Andrew Edward Caldwell ,  Islam Mohamed Hatem A Atta ,  Valentin Flunkert ,  Arjun Ashok

IPC: G06F16/00 ,  G06F16/2455 ,  G06F16/28 ,  G06F16/2453

CPC classification number: G06F16/24552 ,  G06F16/24539 ,  G06F16/24568 ,  G06F16/283 ,  G06F16/285

Abstract: Placement decisions may be made to place data in a multi-tenant cache. Usage of multi-tenant cache nodes for performing access requests may be obtained. Usage prediction techniques may be applied to the usage to determine placement decisions for data amongst the multi-tenant cache nodes. Placement actions for the data amongst at the multi-tenant cache nodes may be performed according to the placement decisions.

公开(公告)号：US20210406671A1

公开(公告)日：2021-12-30

申请号：US16912312

申请日：2020-06-25

Applicant: Amazon Technologies, Inc.

Inventor： Jan Gasthaus ,  Mohamed El Fadhel Ayed ,  Lorenzo Stella ,  Tim Januschowski

IPC: G06N3/08 ,  G06F16/23 ,  G06N3/04 ,  G06F40/20

Abstract: Techniques for anomaly detection are described. An exemplary method includes receiving a request to monitor for anomalies from one or more data sources; analyzing time-series data from the one or more data sources; generating a recommendation for handling the determined anomaly, the recommendation generated by performing one or more of a root cause analysis, a heuristic analysis, and an incident similarity analysis; and reporting the anomaly and recommendation

公开(公告)号：US11120361B1

公开(公告)日：2021-09-14

申请号：US15441924

申请日：2017-02-24

Applicant: Amazon Technologies, Inc.

Inventor： Tim Januschowski ,  Joos-Hendrik Boese ,  Jan Alexander Gasthaus ,  Sebastian Schelter

IPC: G06N20/00 ,  G06N7/00

Abstract: An input data set with a plurality of item descriptors comprising respective time series observations is identified. A routing directive indicating a predicate to be evaluated to determine whether a particular item descriptor is to be included in a training data set for a first learning algorithm is obtained. A plurality of learning algorithms are trained using training data sets derived from the input data set according to respective routing directives, and the trained algorithms are stored.