Abstract: An inference system includes: a learning unit that generates an inference model using a first dataset including a plurality of sample data whose ground truth is given and a second dataset including a plurality of sample data whose ground truth is not given; and an inference unit that determines an inference result. The inference model includes an encoder that calculates from sample data a first feature value independent of the first and second datasets and a second feature value dependent on the first or second dataset. The learning unit trains the encoder such that the same first feature value is calculated from any of first and second sample data for a pair of the first sample data among the first dataset and the second sample data which is among the second dataset and whose ground truth is to be identical to the ground truth of the first sample data.

Abstract: A model generation apparatus according to one aspect of the present invention acquires a plurality of learning datasets each constituted by a first sample of a first time of predetermined data obtained in time series and feature information included in a second sample of the predetermined data of a future second time relative to the first time, and trains a prediction model, by machine learning, to predict feature information of the second time from the first sample of the first time, for each learning dataset. In the model generation apparatus, a rarity degree for is set each learning dataset, and, in the machine learning, the model generation apparatus trains more preponderantly on learning datasets having a higher rarity degree.