Hitachi, Ltd.

Wenpeng Wei

Toshiko Aizono

Yuya Okadome

Yasuharu Namba

Kazuaki Tokunaga

Takuya Mogawa

An error in a demand prediction of items is reduced. A data processing apparatus for processing data on a demand for multiple items includes a model learning unit that generates a prediction model for predicting the demand for the multiple items, and a demand prediction unit that predicts the demand for the multiple items by using the prediction model. The model learning unit inputs actual data of the demand for each item to a first neural network of each item, extracts a feature quantity of each item, and combines the feature quantity of each item to generate a second neural network that is a prediction model.

Data processing apparatus and data processing method

In view of the relevant background art, the present invention is to provide a system or apparatus which enables data in use for learning to be sorted out, thereby reducing the amount of data to be handled. Presenting one example of the control system according to the present invention, the control system including a first processing device to generate a learnt model; and a second processing device to operate employing the generated learnt model and provided with input means, wherein the second processing device transmits information input from the input means to the first processing device; and where the input information as transmitted is information belonging to an area different from an area designated with the learnt model, the first processing device generates an updated learnt model based on the information belonging to the different area as received and the learnt model.

Control system and control method

When multiple explanatory variables are automatically created, a huge amount of output suggestions causes a heavy burden on selecting the suggestion. A marketing support system is configured to include a suggestion extraction unit that accepts purchase data and analyzes a correlation between the purchase data to output a composite variable, a restriction filtering unit that accepts the composite variable and a restriction table to exclude the composite variable based on a restriction condition defined in the restriction table, and a result filtering unit that uses a measure result defined in the past to estimate an anticipated effect when a measure based on the composite variable is performed, and selects a plurality of explanatory variables.

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Tokyo, Japan

Wenpeng Wei