Abstract: A learning device generates a first learned model by subjecting a learning model to learning such that a correlation coefficient between a first explanatory variable for learning and an explained variable output from the learning model is maximized when the learning model that outputs an explained variable in a case where an explanatory variable is input is subjected to learning on the basis of first learning data representing a pair of the first explanatory variable for learning and a first explained variable for learning. When the first learned model is subjected to relearning on the basis of the second learning data representing the pair of the second explanatory variable for learning and the second explained variable for learning, the learning device generates the second learned model by subjecting the first learned model to relearning such that the error between the second explained variable for learning and the explained variable output from the first learned model is minimized.

Abstract: An estimation device collects sensor data representing a change in a load along a time series as a change in a voltage value from a sensor that generates a voltage when a load applied to an area changes, and estimates a matter related to a measurement target passing through a predetermined area by using sensor data collected from the sensor installed in the predetermined area.

Abstract: A prediction method of performing heat source demand prediction in a space having a predetermined air conditioning control region, includes predicting a required heat quantity in the air conditioning control region by using a predetermined parameter related to a surrounding environment of the air conditioning control region and a setting value of an air conditioner set in the air conditioning control region as inputs, and predicting a heat source demand of the entire space from the predicted required heat quantity for each air conditioning control region.

Abstract: The present disclosure is intended to enable playback of media data corresponding to a sensibility effect set in advance.

Abstract: Sufficient data extension can be achieved even if the starting date/time and ending date/time of the learning execution data have been uniquely defined. Based on entire learning data that is a set of time-series data, wherein the entire learning data is a set of minimum constitution unit data, each of which is time-series data having a first time interval that is a time interval required for learning, wherein the time-series data having the first time interval is each assigned a first label that indicates a feature in a time series of the first interval; a generation unit (103) generates learning execution data that is a set of time-series data to be used in learning, by combining the minimum constitution unit data included in the entire learning data such that regularity of the first label in a time series of the entire learning data is maintained.

Abstract: The present disclosure is intended to enable playback of media data corresponding to a sensibility effect set in advance.