Change points of a system represented by a plurality of time series are detected more appropriately. An information processing system includes means for learning, with respect to each of a plurality of time series, models that approximate partial time series respectively and are defined by parameters of the partial time series respectively, the partial time series being obtained by dividing a corresponding time series into a plurality of segments at change point candidates; and means for detecting, with respect to each of the change point candidates for the plurality of time series, a global change point that is a change point for the plurality of time series based on a difference between a parameter of a first partial time series starting from a time point of a corresponding change point candidate and a parameter of a second partial time series before the corresponding change point candidate, and outputting the global change point.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An information processing system comprising: a memory storing instructions, and one or more processors configured to execute the instructions to: learn, with respect to each of a plurality of time series, models that approximate partial time series respectively and are defined by parameters of the partial time series respectively, the partial time series being obtained by dividing a corresponding time series into a plurality of segments at change point candidates; and detect, with respect to each of the change point candidates for the plurality of time series, a global change point that is a change point for the plurality of time series based on a difference between a parameter of a first partial time series starting from a time point of a corresponding change point candidate and a parameter of a second partial time series before the corresponding change point candidate, and output the global change point. 2. The information processing system according to claim 1, wherein the one or more processors configured to further execute the instructions to: set a loss function with respect to approximate values for values of a time series, the approximate values being calculated based on the parameters; and set a first penalty term whose value increases as an amount of change in the parameters of a single time series out of the plurality of time series increases and a second penalty term whose value decreases as the number of time series whose parameters change at a predetermined time increases out of the plurality of time series, and parameters that minimize a sum of the loss function, the first penalty term, and the second penalty term for respective partial time series of all time series in the plurality of time series are calculated by using an optimization algorithm. 3. The information processing system according to claim 2, wherein as the first penalty term, an absolute value sum of amounts of change in parameters at the respective change point candidates is set. 4. The information processing system according to claim 2, wherein as the second penalty term, a square root of a square sum of amounts of change in parameters at the respective change point candidates, the square sum being taken over all time series is set. 5. The information processing system according to claim 2, wherein as the optimization algorithm, an alternating direction method of multipliers is used. 6. The information processing system according to claim 1, wherein with respect to the global change point, information indicating how and which time series causes the change point candidate to be detected as the global change point is outputted. 7. A change point detection method performed by a computer comprising: learning, with respect to each of a plurality of time series, models that approximate partial time series respectively and are defined by parameters of the partial time series respectively, the partial time series being obtained by dividing a corresponding time series into a plurality of segments at change point candidates; and detecting, with respect to each of the change point candidates for the plurality of time series, a global change point that is a change point for the plurality of time series based on a difference between a parameter of a first partial time series starting from a time point of a corresponding change point candidate and a parameter of a second partial time series before the corresponding change point candidate, and outputting the global change point. 8. A non-transitory computer readable storage medium recording thereon a program causing a computer to perform a method comprising: learning, with respect to each of a plurality of time series, models that approximate partial time series respectively and are defined by parameters of the partial time series respectively, the partial time series being obtained by dividing a corresponding time series into a plurality of segments at change point candidates; and detecting, with respect to each of the change point candidates for the plurality of time series, a global change point that is a change point for the plurality of time series based on a difference between a parameter of a first partial time series starting from a time point of a corresponding change point candidate and a parameter of a second partial time series before the corresponding change point candidate, and outputting the global change point.