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    Time Series Forecasting
    1.
    发明公开
    Time Series Forecasting 审中-公开

    公开(公告)号：US20230297583A1

    公开(公告)日：2023-09-21

    申请号：US18323766

    申请日：2023-05-25

    Applicant: Google LLC

    Inventor： Xi Cheng ,  Amir H. Hormati ,  Lisa Yin ,  Umar Syed

    IPC: G06F16/2458 ,  G06F16/22

    CPC classification number: G06F16/2477 ,  G06F16/221 ,  G06F16/2282

    Abstract: A method for time series forecasting includes receiving a time series forecasting query from a user requesting the data processing hardware to perform a plurality of time series forecasts. Each time series forecast is a forecast of future data based on respective current data. Simultaneously, for each time series forecast of the plurality of time series forecasts requested by the time series forecasting query, the method includes training a plurality of models for the respective time series forecast. The method also includes determining which model of the plurality of models best fits the respective time series forecast and forecasting the future data based on the determined best fitting model and the respective current data. The method also includes returning, to the user, the forecasted future data for each of the plurality of time series forecasts request by the timer series forecasting query.

    Machine Learning Regression Analysis
    2.
    发明申请
    Machine Learning Regression Analysis 有权

    公开(公告)号：US20230094479A1

    公开(公告)日：2023-03-30

    申请号：US17449660

    申请日：2021-09-30

    Applicant: Google LLC

    Inventor： Xi Cheng ,  Lisa Yin ,  Mingge Deng ,  Amir Hormati ,  Umar Ali Syed ,  Jiashang Liu

    IPC: G06N7/00 ,  G06K9/62

    Abstract: A method includes receiving a model analysis request from a user. The model analysis requests requesting the data processing hardware to provide one or more statistics of a model trained on a dataset. The method also includes obtaining the trained model. The trained model includes a plurality of weights. Each weight is assigned to a feature of the trained model. The model also includes determining, using the dataset and the plurality of weights, the one or more statistics of the trained model based on a linear regression of the trained model. The method includes reporting the one or more statistics of the trained model to the user.

    Time series forecasting
    3.
    发明授权
    Time series forecasting 有权

    公开(公告)号：US11693867B2

    公开(公告)日：2023-07-04

    申请号：US16986861

    申请日：2020-08-06

    Applicant: Google LLC

    Inventor： Xi Cheng ,  Amir H. Hormati ,  Lisa Yin ,  Umar Syed

    IPC: G06F16/2458 ,  G06F16/22

    CPC classification number: G06F16/2477 ,  G06F16/221 ,  G06F16/2282

    Abstract: A method for time series forecasting includes receiving a time series forecasting query from a user requesting the data processing hardware to perform a plurality of time series forecasts. Each time series forecast is a forecast of future data based on respective current data Simultaneously, for each time series forecast of the plurality of time series forecasts requested by the time series forecasting query, the method includes training a plurality of models for the respective time series forecast. The method also includes determining which model of the plurality of models best fits the respective time series forecast and forecasting the future data based on the determined best fitting model and the respective current data. The method also includes returning, to the user, the forecasted future data for each of the plurality of time series forecasts request by the timer series forecasting query.

    Time Series Forecasting
    4.
    发明申请
    Time Series Forecasting 有权

    公开(公告)号：US20210357402A1

    公开(公告)日：2021-11-18

    申请号：US16986861

    申请日：2020-08-06

    Applicant: Google LLC

    Inventor： Xi Cheng ,  Amir H. Hormati ,  Lisa Yin ,  Umar Syed

    IPC: G06F16/2458 ,  G06F16/22

    Abstract: A method for time series forecasting includes receiving a time series forecasting query from a user requesting the data processing hardware to perform a plurality of time series forecasts. Each time series forecast is a forecast of future data based on respective current data Simultaneously, for each time series forecast of the plurality of time series forecasts requested by the time series forecasting query, the method includes training a plurality of models for the respective time series forecast. The method also includes determining which model of the plurality of models best fits the respective time series forecast and forecasting the future data based on the determined best fitting model and the respective current data The method also includes returning, to the user, the forecasted future data for each of the plurality of time series forecasts request by the timer series forecasting query.

    Machine Learning Super Large-Scale Time-series Forecasting
    6.
    发明公开
    Machine Learning Super Large-Scale Time-series Forecasting 审中-公开

    公开(公告)号：US20230274180A1

    公开(公告)日：2023-08-31

    申请号：US17652863

    申请日：2022-02-28

    Applicant: Google LLC

    Inventor： Xi Cheng ,  Jiashang Liu ,  Lisa Yin ,  Amir Hossein Hormati ,  Mingge Deng ,  Weijie Shen ,  Kashif Yousuf

    IPC: G06N20/00 ,  G06F16/248

    CPC classification number: G06N20/00 ,  G06F16/248

    Abstract: A method for forecasting time-series data, when executed by data processing hardware, causes the data processing hardware to perform operations including receiving a time series forecasting query from a user requesting a time series forecast forecasting future data based on a set of current time-series data. The operations include obtaining, from the set of current time-series data, a set of training data. The operations include training, using a first portion of the set of training data, a first sub-model of a forecasting model and training, using a second portion of the set of training data, a second sub-model of the forecasting model. The second portion is different than the first portion. The operations include forecasting, using the forecasting model, the future data based on the set of current time-series data and returning, to the user, the forecasted future data for the time series forecast.

    EXPLAINABLE ARTIFICIAL INTELLIGENCE IN COMPUTING ENVIRONMENT
    8.
    发明申请
    EXPLAINABLE ARTIFICIAL INTELLIGENCE IN COMPUTING ENVIRONMENT 有权

    公开(公告)号：US20220405623A1

    公开(公告)日：2022-12-22

    申请号：US17354392

    申请日：2021-06-22

    Applicant: Google LLC

    Inventor： Xi Cheng ,  Lisa Yin ,  Jiashang Liu ,  Amir H. Hormati ,  Mingge Deng ,  Christopher Avery Meyers

    IPC: G06N5/04 ,  G06K9/62 ,  G06F16/245 ,  G06N20/00

    Abstract: The disclosure is directed to a query-driven machine learning platform for generating feature attributions and other data for interpreting the relationship between inputs and outputs of a machine learning model. The platform can receive query statements for selecting data, training a machine learning model, and generating model explanation data for the model. The platform can distribute processing for generating the model explanation data to scale in response to requests to process selected data, including multiple records with a variety of different feature values. The interface between a user device and the machine learning platform can streamline deployment of different model explainability approaches across a variety of different machine learning models.

    SCALABLE MATRIX FACTORIZATION IN A DATABASE
    9.
    发明申请
    SCALABLE MATRIX FACTORIZATION IN A DATABASE 审中-公开

    公开(公告)号：US20200320072A1

    公开(公告)日：2020-10-08

    申请号：US16843334

    申请日：2020-04-08

    Applicant: Google LLC

    Inventor： Amir H. Hormati ,  Lisa Yin ,  Umar Ali Syed ,  Mingge Deng

    IPC: G06F16/2453 ,  G06Q30/02 ,  G06F17/16 ,  G06F16/22 ,  G06K9/62 ,  G06N5/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for scalable matrix factorization. A method includes obtaining a Structured Query Language (SQL) query to create a matrix factorization model based on a set of training data, generating SQL sub-queries that don't include non-scalable functions, obtaining the set of training data, and generating a matrix factorization model based on the set of training data and the SQL sub-queries that don't include non-scalable functions.

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