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公开(公告)号:US20250086389A1
公开(公告)日:2025-03-13
申请号:US18367310
申请日:2023-09-12
Inventor: Gayathri SARANATHAN , Nway Nway AUNG , Ariel BECK , Chandra Suwandi WIJAYA , Jianyu CHEN , Debdeep PAUL , Sahim YAMAURA , Koji MIURA
IPC: G06F40/279 , G06N20/00
Abstract: According to an embodiment, a method for generating textual features corresponding to text documents from a raw dataset is disclosed. The method includes preprocessing the text documents and determining topic probability scores (TPS) and confidence scores (CS) using unsupervised and supervised machine learning models, respectively. The combination of TPS and CS is used to generate a compound distribution score (CDS), which forms a comprehensive representation of the output of the machine learning models. The determined TPS, CS, and CDS are then used to generate a set of textual features, which serve as independent variables for a forecasting model.
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公开(公告)号:US20240320694A1
公开(公告)日:2024-09-26
申请号:US18124302
申请日:2023-03-21
Inventor: Debdeep PAUL , Sahim YAMAURA , Chandra Suwandi WIJAUA
IPC: G06Q30/0202 , G06N7/01
CPC classification number: G06Q30/0202 , G06N7/01
Abstract: According to an embodiment, a method for generating and explaining trend forecast of a timeseries with measures of quality of explainability is disclosed. The method comprises receiving a target variable and a set of relevant feature(s) corresponding to the variable. The method comprises performing a classification for the target variable, wherein the classification indicates classifying the target variable into a one or more states. Further, the method comprises determining a state transition matrices for each timestamp and design appropriate functions to model and quantify the trend forecast via a state transition score. The state transition score indicates transition between the corresponding states, wherein states may be obtained through suitable encoding of the target variable, and generating and explaining trend forecast based on the state transition.
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