Abstract: A demand prediction device calculates time-series data of a prediction value of future demand for durable goods belonging to a particular broad division through time-series analysis, constructs a model for calculating a purchase probability based on attribute information, information on types of durable goods purchased by the users, and time information on the purchase, calculates purchase probability data including a purchase probability of the durable goods in each of time periods for each of users and a purchase probability for the broad division and each of the plurality of subdivisions of durable goods for each of the users by inputting the attribute information of the users to the model, and calculates and outputs a prediction value of the total demand for each of the plurality of subdivisions in a particular time period based on the time-series data of the prediction value and the purchase probability data.

Abstract: A price prediction device includes an attribute acquisition unit that acquires data indicating attributes of consumers, a history acquisition unit that acquires data indicating a history of a product purchased by the consumer and a purchase price of the product, a probability acquisition unit that derives a selection probability of a target product being selected from a product group for each consumer based on the attribute and the history, and a derivation unit that derives an optimal price of the target product for each consumer based on the selection probability of the target product and a selling price of the target product, by using a price prediction model constructed by machine learning.