A first learning unit (101) learns a difference model (111) for predicting a difference between current monitoring data that is monitoring data obtained by monitoring a monitoring target at each time point and at each of a plurality of monitoring points and is monitoring data at a current time point, and past monitoring data that is monitoring data at each of a plurality of past time points; a second learning unit (102) learns a prediction model (past) (112) for predicting variation of the monitoring target using the past monitoring data; a first generation unit (103) generates corrected past data using the difference model (111), by correcting a difference between the past monitoring data and the current monitoring data; and a third learning unit (104) learns a prediction model (current) (113) for predicting variation of the monitoring target using the current monitoring data, the difference model (111), the prediction model (past) (112), and the corrected past data, whereby variation of the monitoring target can be appropriately predicted even when the monitoring target involves irregular variation.


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    Title :

    LEARNING DEVICE, PREDICTION DEVICE, LEARNING METHOD, AND LEARNING PROGRAM


    Contributors:

    Publication date :

    2022-11-17


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS



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