A prediction apparatus comprising: a data acquisition unit configured to acquire operation data indicating an operation state of a device (5A-5C); a probability density estimation unit configured to estimate a probability density of the operation data; and an abnormality prediction unit configured to predict whether or not an abnormality occurs in the device (5A-5C), based on an estimation result of the probability density of the operation data and a first prediction model, characterized in that the device (5A-5C) is gas engine and the abnormality is a misfire occurring in a cylinder of the gas engine, the data acquisition unit is configured to acquire second operation data and a warning data for a first predetermined period, the probability density estimation unit is configured to apply a variational Bayesian method to the second operation data to estimate second probability density values of the second operation data, and the prediction apparatus further comprises: a prediction model creation unit which is configured to perform a pre-process in which with reference to the warning data, label information of a misfire occurrence is attached to estimated probability density values for a period between a first day when a misfire has actually occurred and a second day that goes back a second predetermined period from the first day, and label information of a no misfire is attached to estimated second probability density values on another days outside the period, wherein the prediction model creation unit is configured to use the estimated second probability density values to which the label information has been attached, as learning data, and to create the first prediction model representing a relationship between the occurrence of a misfire and a probability density. a data acquisition unit configured to acquire operation data indicating an operation state of a device (5A-5C); a probability density estimation unit configured to estimate a probability density of the operation data; and an abnormality prediction unit configured to predict whether or not an abnormality occurs in the device (5A-5C), based on an estimation result of the probability density of the operation data and a first prediction model, characterized in that the device (5A-5C) is gas engine and the abnormality is a misfire occurring in a cylinder of the gas engine, the data acquisition unit is configured to acquire second operation data and a warning data for a first predetermined period, the probability density estimation unit is configured to apply a variational Bayesian method to the second operation data to estimate second probability density values of the second operation data, and the prediction apparatus further comprises: a prediction model creation unit which is configured to perform a pre-process in which with reference to the warning data, label information of a misfire occurrence is attached to estimated probability density values for a period between a first day when a misfire has actually occurred and a second day that goes back a second predetermined period from the first day, and label information of a no misfire is attached to estimated second probability density values on another days outside the period, wherein the prediction model creation unit is configured to use the estimated second probability density values to which the label information has been attached, as learning data, and to create the first prediction model representing a relationship between the occurrence of a misfire and a probability density. a prediction model creation unit which is configured to perform a pre-process in which with reference to the warning data, label information of a misfire occurrence is attached to estimated probability density values for a period between a first day when a misfire has actually occurred and a second day that goes back a second predetermined period from the first day, and label information of a no misfire is attached to estimated second probability density values on another days outside the period, wherein the prediction model creation unit is configured to use the estimated second probability density values to which the label information has been attached, as learning data, and to create the first prediction model representing a relationship between the occurrence of a misfire and a probability density.