An information processing system includes: a setting unit to set a normal range showing a range of normal values of monitoring target data of time series signals by defining an upper limit and a lower limit; a determination unit to determine whether the monitoring target data is out of the normal range or not and to feed the determined time which is output in case of deviation and which is judged to be the time for the monitoring target data to turn to be out of the normal range; and a detection unit to determine the start time that is before the determined time entering from the determination unit and which is the time for the monitoring target data to start to show an anomaly on the basis of the degree of deviation showing a deviation of the monitoring target data from a mean of multiple learning data which consist of normal value signals from among already-acquired monitoring target data. This enables the system to more accurately determine the time for the signal to start to show an anomaly.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An information processing system comprising: a setting unit to set a normal range showing a range of normal values of monitoring target data of time series signals by defining an upper limit and a lower limit; a determination unit to determine whether the monitoring target data is out of the normal range or not and to feed a determined time which is output in case of deviation and which is judged to be the time for the monitoring target data to turn to be out of the normal range; and a detection unit to determine a start time that is before the determined time entering from the determination unit and which is a time for the monitoring target data to start to show an anomaly on the basis of a degree of deviation showing a deviation of the monitoring target data from a mean of multiple learning data which include normal value signals from among already-acquired monitoring target data. 2. The information processing system set forth in claim 1, wherein the setting unit chooses to set the maximum value among the multiple learning data values for the upper limit at each of multiple points in time, and chooses to set the minimum value among the multiple learning data values for the lower limit at each of the multiple points in time. 3. The information processing system set forth in claim 1, wherein the setting unit sets the upper limit common to multiple points in time, and sets the lower limit common to multiple points in time. 4. The information processing system set forth in claim 1, wherein the setting unit defines the upper limit and the lower limit with some constant differences from the mean of the multiple learning data at the multiple points in time. 5. The information processing system set forth in claim 1, wherein the setting unit computes characteristic quantities based on a correlation coefficient of the multiple learning data to define the upper limit and the lower limit according to the range of the characteristic quantities. 6. The information processing system set forth in claim 1, wherein the setting unit computes characteristic quantities based on Mahalanobis distances of the multiple learning data to define the upper limit and the lower limit according to the range of the characteristic quantities. 7. The information processing system set forth in claim 1, wherein the detection unit determines the start time by choosing a time that is before the determined time and at which the inclination of the degree of deviation reaches or exceeds a first threshold. 8. The information processing system set forth in claim 7, wherein the detection unit determines the start time after smoothing the inclination of the degree of deviation. 9. The information processing system set forth in claim 1, wherein the detection unit determines the start time by choosing a time at which the inclination of the degree of deviation reaches or exceeds the first threshold and the degree of deviation reaches or exceeds a second threshold where the time is before the determined time. 10. The information processing system set forth in claim 9, wherein the detection unit determines the start time after smoothing the degree of deviation or the inclination of the degree of deviation. 11. The information processing system set forth in claim 1, wherein the detection unit determines the start time based on the degree of deviation using the Bayesian change point detection algorithm. 12. The information processing system set forth in claim 1, further comprising an inference unit to draw an inference that the monitoring target data with the earliest start time from among start times of multiple monitoring target data entering from the detection unit is an anomaly-origin-representing signal. 13. The information processing system set forth in claim 12, wherein the inference unit keeps a list of physical causes and effects relating multiple monitoring target data to infer the anomaly-origin-representing monitoring target data based on the list when the monitoring target data entering from the detection unit is in the list. 14. The information processing system set forth in claim 1, further comprising a display unit to display monitoring target data with the determined time thereof which is an output of the determination unit and with the start time thereof determined by the detection unit on a graph. 15. The information processing system set forth in claim 1, further comprising a display unit to display the start times for multiple monitoring target data, determined by the detection unit. 16. An information processing method comprising: setting a normal range showing a range of normal values of monitoring target data of time series signals by defining an upper limit and a lower limit; determining whether the monitoring target data is out of the normal range or not and feeding a determined time which is output in case of deviation and which is judged to be the time for the monitoring target data to turn to be out of the normal range; and determining a start time that is before the determined time and which is a time for the monitoring target data to start to show an anomaly on the basis of a degree of deviation showing a deviation of the monitoring target data from a mean of multiple learning data which consist of normal value signals from among already-acquired monitoring target data.