Abstract: An information processing device incudes one or more hardware processors. The processors calculate first similarities between a plurality of probabilistic models each modeling a probability of a value, at corresponding time, of time series data whose data length is a specific value, and a plurality of pieces of partial time series data whose data length is the specific value, the plurality of pieces of partial time series data being contained in target time series data to be a target of diagnosis; and determine a plurality of pieces of matching information including positions of the plurality of pieces of partial time series data in the target time series data, first probabilistic models whose first similarities with respect to the plurality of pieces of partial time series data at the positions are larger than other probabilistic models, and the first similarities to the first probabilistic models.

Abstract: An information processing apparatus according to one embodiment includes one or more hardware processors connected to a memory. The hardware processors functions to store, in the memory, history information including identification information of a model and a history of updating the model. The model receives input data including variables and outputs output data. The variables are each a variable for which a rate of influence on the output data is calculated. The model has been updated by using first input data. The hardware processors functions to select a target model to be updated by using second input data. The target model is selected from among models identified by their respective identification information. The hardware processors functions to update the target model by performing transfer learning in which updated parameters are estimated by using the second input data.