An object is to make it possible to learn a prediction model for precisely predicting the number of passersby at a prediction time point. A learning unit 300 learns parameters of the prediction model with respect to each of a plurality of observation points based on learning data such that the number of passersby at the observation point at a prediction time point predicted using the prediction model matches the number of passersby at a time point corresponding to the prediction time point included in the learning data. The learning data includes, with respect to each observation point, a past time point of the observation point and the number of passersby at the observation point at the past time point. The prediction model has parameters including a first weight indicating the influence of the numbers of passersby at other observation points based on distances to the other observation points, a second weight indicating the influence of the numbers of passersby at the other observation points based on a travel route, and a third weight differing from the first weight and the second weight and indicating the influence of the numbers of passersby at the other observation points.


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    Title :

    TIME-SERIES LEARNING DEVICE, TIME-SERIES LEARNING METHOD, TIME-SERIES PREDICTION DEVICE, TIME-SERIES PREDICTION METHOD, AND PROGRAM


    Contributors:

    Publication date :

    2021-09-02


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

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



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