公开(公告)号：US20220391754A1

公开(公告)日：2022-12-08

申请号：US17370388

申请日：2021-07-08

Applicant: Oracle International Corporation

Inventor： Beiwen Guo ,  Matthew T. Gerdes ,  Guang C. Wang ,  Hariharan Balasubramanian ,  Kenny C. Gross

IPC: G06N20/00 ,  G06N5/04

Abstract: The disclosed embodiments relate to a system that produces anomaly-free training data to facilitate ML-based prognostic surveillance operations. During operation, the system receives a dataset comprising time-series signals obtained from a monitored system during normal, but not necessarily fault-free operation of the monitored system. Next, the system divides the dataset into subsets. The system then identifies subsets that contain anomalies by training one or more inferential models using combinations of the subsets, and using the one or more trained inferential models to detect anomalies in other target subsets of the dataset. Finally, the system removes any identified subsets from the dataset to produce anomaly-free training data.

公开(公告)号：US20240362210A1

公开(公告)日：2024-10-31

申请号：US18139492

申请日：2023-04-26

Applicant: Oracle International Corporation

Inventor： Ankit Aggarwal ,  Jie Xing ,  Chirag Ahuja ,  Vikas Pandey ,  Hariharan Balasubramanian

IPC: G06F16/242 ,  G06F16/2455

CPC classification number: G06F16/244 ,  G06F16/24553

Abstract: Techniques are described herein for forecasting datasets using blend of temporal aggregation and grouped aggregation. An example method can include a device accessing a first and second time series, comprising a first data point associated with a first time step and a first value and a second data point associated with a second time step and a second value. The method can further include the device determining a grouped aggregated data point using the first and second time series by aligning the first and second data point. The method can further include the device determining the grouped aggregated data point by summing the first and second value. The method can further include determining a grouped aggregated time series. The method can further include the device determining a first set of input values for a machine learning model. The method can further include the device determining a first forecasted future value.

公开(公告)号：US20240118965A1

公开(公告)日：2024-04-11

申请号：US17962869

申请日：2022-10-10

Applicant: Oracle International Corporation

Inventor： Shwan Ashrafi ,  Michal Piotr Prussak ,  Hariharan Balasubramanian ,  Vijayalakshmi Krishnamurthy

IPC: G06F11/07

CPC classification number: G06F11/079 ,  G06F11/0712

Abstract: Techniques are described for identifying root cause anomalies in time series. Information to be used for root cause analysis (RCA) is obtained from a graph neural network (GNN) and is used to construct a dependency graph having nodes corresponding to each time series and directed edges corresponding to dependencies between the time series. Nodes corresponding to time series that do not contain anomalies may be removed from this dependency graph, as well as edges connected to these nodes. This edge and node removal may result in the creation of one or more sub-graphs from the dependency graph. A root cause analysis algorithm may be run on these one or more sub-graphs to create a root cause graph for each sub-graph. These root cause graphs may then be used to identify root cause anomalies within the multiple time series, as well as sequences of anomalies within the multiple time series.

公开(公告)号：US20230385663A1

公开(公告)日：2023-11-30

申请号：US18323339

申请日：2023-05-24

Applicant: Oracle International Corporation

Inventor： Chirag Ahuja ,  Vikas Rakesh Upadhyay ,  Samik Raychaudhuri ,  Syed Fahad Allam Shah ,  Hariharan Balasubramanian

IPC: G06N5/045

CPC classification number: G06N5/045

Abstract: A time series forecasting system is disclosed that obtains a time series forecast request requesting a forecast for a particular time point. The forecast request identifies a primary time series dataset for generating the requested forecast and a set of features related to the primary time series dataset. The system provides the primary time series dataset and the set of features to a model to be used for generating the forecast. The model computes a feature importance score for one or more features and selects a subset of features based on their feature importance scores. The model determines attention scores for a set of data points in the primary time series dataset based on the selected subset of features. The system predicts an actual forecast for the particular time point based on the attention scores and outputs the actual forecast and explanation information associated with the actual forecast.