PREDICTING REGIONAL VIEWERSHIP FOR BROADCAST MEDIA EVENTS

    公开(公告)号:US20220385387A1

    公开(公告)日:2022-12-01

    申请号:US17704566

    申请日:2022-03-25

    发明人: Xuxu Wang Yu Tian

    摘要: Techniques for regional viewership predictions of broadcast events such as live broadcast professional sporting events. The techniques can make the predictions without a direct response variable such as regional viewership data for training a prediction model. Instead, in one technique, demand information for a good or service is used. From the demand information, a derivative demand for the good or service relative to a normal demand is determined. A regression framework is used to learn relationships between the derivative demand for the good or service and features of past live broadcast sporting events. This results in a matrix of feature weights. A non-parametric mixture framework is then used to find a set of feature weights that can be applied to features of future broadcast events to generate regional viewership predictions for the events.

    Predicting regional viewership for broadcast media events

    公开(公告)号:US11522625B1

    公开(公告)日:2022-12-06

    申请号:US17704566

    申请日:2022-03-25

    发明人: Xuxu Wang Yu Tian

    摘要: Techniques for regional viewership predictions of broadcast events such as live broadcast professional sporting events. The techniques can make the predictions without a direct response variable such as regional viewership data for training a prediction model. Instead, in one technique, demand information for a good or service is used. From the demand information, a derivative demand for the good or service relative to a normal demand is determined. A regression framework is used to learn relationships between the derivative demand for the good or service and features of past live broadcast sporting events. This results in a matrix of feature weights. A non-parametric mixture framework is then used to find a set of feature weights that can be applied to features of future broadcast events to generate regional viewership predictions for the events.

    Iterative singular spectrum analysis

    公开(公告)号:US12026220B2

    公开(公告)日:2024-07-02

    申请号:US17860844

    申请日:2022-07-08

    发明人: Xuxu Wang Xiping Fu

    IPC分类号: G06F17/18

    CPC分类号: G06F17/18

    摘要: Techniques for an iterative singular spectrum analysis are provided. In one technique, a first analysis, of time series data, is performed that results in a first reconstructed version of the time series data. The first analysis, of the time series data and a portion of the first reconstructed version, is then performed that results in a second reconstructed version of the time series data. Based on a termination condition, it is determined whether to perform the first analysis relative to a portion of a third reconstructed version of the time series data. A second analysis, of the time series data and a portion of a fourth reconstructed version of the time series data, is performed that results in a fifth reconstructed version of the time series data. The second analysis is different than the first analysis. A difference between the time series data and the fifth reconstructed version data is computed.

    ITERATIVE SINGULAR SPECTRUM ANALYSIS
    5.
    发明公开

    公开(公告)号:US20240012876A1

    公开(公告)日:2024-01-11

    申请号:US17860844

    申请日:2022-07-08

    发明人: Xuxu Wang Xiping Fu

    IPC分类号: G06Q30/02

    CPC分类号: G06Q30/0201

    摘要: Techniques for an iterative singular spectrum analysis are provided. In one technique, a first analysis, of time series data, is performed that results in a first reconstructed version of the time series data. The first analysis, of the time series data and a portion of the first reconstructed version, is then performed that results in a second reconstructed version of the time series data. Based on a termination condition, it is determined whether to perform the first analysis relative to a portion of a third reconstructed version of the time series data. A second analysis, of the time series data and a portion of a fourth reconstructed version of the time series data, is performed that results in a fifth reconstructed version of the time series data. The second analysis is different than the first analysis. A difference between the time series data and the fifth reconstructed version data is computed.