Learning apparatus, learning method, computer program and recording medium

A learning method in a learning apparatus that performs machine learning by using time-series numerical data as input data,

• the learning method comprising:
• generating a learning model for predicting and outputting time-series numerical data corresponding to inputted time-series numerical data, by performing the machine learning by using normal data that are time-series numerical data indicating a normal state as the input data;
• comparing predicted normal data in a second period corresponding to a first period, which are normal data predicted by the learning model by inputting, into the learning model, normal data in the first period out of the normal data, with normal data in the second period out of the normal data to obtain a first deviation degree indicating an extent of deviation between the normal data in the second period and the predicted normal data;
• comparing predicted abnormal data in a fourth period corresponding to a third period, which are abnormal data predicted by the learning model by inputting, into the learning model, abnormal data in the third period out of abnormal data that are time-series numerical data indicating an abnormal state, with abnormal data in the fourth period out of the abnormal data to obtain a second deviation degree indicating an extent of deviation between the abnormal data in the fourth period and the predicted abnormal data;
• and
• detecting an insufficient time-series pattern from among time-series patterns indicating the normal state relating to the normal data, on the basis of the first deviation degree and the second deviation degree.

A non-transitory recording medium on which a computer program is recorded,

• the computer program allowing a computer to execute a learning method,
• the learning method being a learning method in a learning apparatus that performs machine learning by using time-series numerical data as input data,
• the learning method comprising:
• generating a learning model for predicting and outputting time-series numerical data corresponding to inputted time-series numerical data, by performing the machine learning by using normal data that are time-series numerical data indicating a normal state as the input data;
• comparing predicted normal data in a second period corresponding to a first period, which are normal data predicted by the learning model by inputting, into the learning model, normal data in the first period out of the normal data, with normal data in the second period out of the normal data to obtain a first deviation degree indicating an extent of deviation between the normal data in the second period and the predicted normal data;
• comparing predicted abnormal data in a fourth period corresponding to a third period, which are abnormal data predicted by the learning model by inputting, into the learning model, abnormal data in the third period out of abnormal data that are time-series numerical data indicating an abnormal state, with abnormal data in the fourth period out of the abnormal data to obtain a second deviation degree indicating an extent of deviation between the abnormal data in the fourth period and the predicted abnormal data;
• and
• detecting an insufficient time-series pattern from among time-series patterns indicating the normal state relating to the normal data, on the basis of the first deviation degree and the second deviation degree.