A method of detecting chattering in a cold rolling mill, the method comprising: a step of predicting occurrence of chattering during rolling of a material to be rolled, by inputting second multidimensional data to a prediction model, the second multidimensional data having been generated based on condition data corresponding to array data related to the material to be rolled, and the prediction model having been trained with an explanatory variable and an objective variable, the explanatory variable being first multidimensional data generated based on one-dimensional array data representing a past rolling record of rolling of rolled materials by means of a cold rolling mill, and the objective variable being a past record of occurrence of chattering corresponding to the past rolling record, wherein the condition data are one-dimensional array data representing rolling conditions for the material to be rolled, or temporal change of an operation state of the cold rolling mill in rolling of the material to be rolled, and the first multidimensional data and the second multidimensional data are data resulting from connection of the condition data or the data resulting from shifting of the condition data in a time direction, in a first direction and/or a second direction, and wherein a time pitch in a case where the condition data are shifted in the time direction is equal to or less than one second and more than 0 seconds. a step of predicting occurrence of chattering during rolling of a material to be rolled, by inputting second multidimensional data to a prediction model, the second multidimensional data having been generated based on condition data corresponding to array data related to the material to be rolled, and the prediction model having been trained with an explanatory variable and an objective variable, the explanatory variable being first multidimensional data generated based on one-dimensional array data representing a past rolling record of rolling of rolled materials by means of a cold rolling mill, and the objective variable being a past record of occurrence of chattering corresponding to the past rolling record, wherein the second multidimensional data having been generated based on condition data corresponding to array data related to the material to be rolled, and the prediction model having been trained with an explanatory variable and an objective variable, the explanatory variable being first multidimensional data generated based on one-dimensional array data representing a past rolling record of rolling of rolled materials by means of a cold rolling mill, and the objective variable being a past record of occurrence of chattering corresponding to the past rolling record, wherein the explanatory variable being first multidimensional data generated based on one-dimensional array data representing a past rolling record of rolling of rolled materials by means of a cold rolling mill, and the objective variable being a past record of occurrence of chattering corresponding to the past rolling record, wherein the condition data are one-dimensional array data representing rolling conditions for the material to be rolled, or temporal change of an operation state of the cold rolling mill in rolling of the material to be rolled, and the first multidimensional data and the second multidimensional data are data resulting from connection of the condition data or the data resulting from shifting of the condition data in a time direction, in a first direction and/or a second direction, and wherein a time pitch in a case where the condition data are shifted in the time direction is equal to or less than one second and more than 0 seconds.