Information processing device, data structure, information processing method, and non-transitory computer readable storage medium

An information processing device, comprising:

• a communication interface configured to acquire first multi-dimensional sequence data including an observation value observed at a time t as one sequence;
• and
• at least one processor configured to generate second multi-dimensional sequence data including an observation value predicted to be observed at a time t+n after the time t as another sequence from the first multi-dimensional sequence data acquired by the communication interface based on a learning model acquired by machine learning using the first multi-dimensional sequence data,
• wherein the machine learning occurs based on a comparison of the first mufti-dimensional sequence data including an observation value observed at a certain target time as the one sequence and the second multi-dimensional sequence data including an observation value predicted to be observed at the target time as the another sequence.

A computer-implemented information processing method, the method comprising:

• acquiring, by a computer, first image data comprising observed observation values observed at a time t as pixel values;
• and
• generating, by the computer, second image data comprising predicted observation values predicted to be observed at a time t+n after the time t as pixel values, the predicated observations values generated from the acquired first image data based on a learning model obtained by machine learning using the first image data,
• wherein the machine learning is based on a comparison of the first image data in which an observation value observed at a target time is used as a pixel value and the second image data in which an observation value predicted to be observed at the target time is used as a pixel value.

A non-transitory computer-readable storage medium having stored therein one or more executable instructions, the one or more instructions comprising:

• acquiring first image data comprising observed observation values observed at a time t as pixel values;
• and
• generating second image data comprising predicted observation values predicted to be observed at a time t+n after the time t as pixel values, the predicated observations values generated from the acquired first image data based on a learning model obtained by machine learning using the first image data,
• wherein the machine learning is based on a comparison of the first image data in which an observation value observed at a target time is used as a pixel value and the second image data in which an observation value predicted to be observed at the target time is used as a pixel value.