Abstract: A training method performed through batch learning by a computer includes: obtaining training data including first time-series data and second time-series data different from the first time-series data; performing first training processing of training a neural process (NP) model, which outputs, using a stochastic process, a prediction result that takes uncertainty into account, to predict first and second time-series data distributions, based on the first time-series data and second time-series data; and performing, using a contrastive learning algorithm, second training processing of (i) training the NP model to bring close to each other first sampling data items generated by sampling from the first time-series data distribution, (ii) training the NP model to bring close to each other second sampling data items generated by sampling from the second time-series data distribution, and (iii) training the NP model to push away the first and second sampling data items far from each other.