A system for forecasting leaks in a fluid-delivery pipeline network. The system identifies a subsystem in the pipeline network that comprises a plurality of topologically connected stations. The system accesses historical temporal sensor measurements of a plurality of variables of the stations that are directly connected and generates a temporal causal dependency model for a first control variable at the first station in the subsystem, based on the plurality of time series of sensor measurements of a second variable of the first station, and temporal delay characteristics of the plurality of time series of sensor measurements of the second variable at the stations directly connected to the first station. The system automatically calculates a normal operating value of the first control variable at the first station and the deviations between actual measured values and the normal operating value and determines a threshold deviation that indicates a leak event.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method for forecasting leaks in a fluid-delivery pipeline network, the method comprising: identifying a subsystem in the pipeline network, the subsystem comprising a plurality of stations that are topologically connected; accessing historical temporal sensor measurements of a plurality of variables from a first station and from stations directly connected to the first station of the plurality of stations in the subsystem, the historical temporal sensor measurements comprising a plurality of time series of sensor measurements of the plurality of variables, the sensor measurements of each time series being captured at corresponding timestamps over a predetermined time-span; generating a temporal causal dependency model for a first control variable of the plurality of variables at the first station in the subsystem, based on the plurality of time series of sensor measurements of a second variable of the plurality of variables at the first station, and temporal delay characteristics of the plurality of time series of sensor measurements of the second variable at the stations directly connected to the first station; automatically calculating a normal operating value of the first control variable at the first station by applying a learning algorithm to train the temporal causal dependency model over a first training period comprising one or more time series of sensor measurements made under normal operating conditions; automatically calculating deviations between actual measured values of the first control variable at the first station and the normal operating value of the first control variable at the first station over the first training period; and determining a threshold deviation of the first control variable that indicates a leak event by calculating the deviations of the first control variable for a second training period comprising a plurality of time series of sensor measurements made under both normal operating conditions and leak event operating conditions. 2. The computer implemented method of claim 1, wherein the plurality of time series of sensor measurements of a second variable of the plurality of variables at the first station are current values of the sensor measurements. 3. The computer implemented method of claim 1, wherein the temporal delay characteristics of the plurality of time series of sensor measurements of the second variable at the stations directly connected to the first station are lagged values of the sensor measurements. 4. The computer implemented method of claim 1, further comprising applying the learning algorithm to the temporal causal dependency model to compute a risk score of the first control variable, the risk score being based on differences between the calculated deviations and the threshold deviation. 5. The computer implemented method of claim 1, further comprising applying the learning algorithm for ranking the time series of sensor measurements of the plurality of variables for generating the temporal causal dependency model for the first control variable. 6. The computer implemented method of claim 1, wherein the historical temporal sensor measurements of the plurality of control variables are accessed from a data repository of a SCADA system monitoring the fluid-delivery pipeline network. 7. The computer implemented method of claim 1, further comprising processing the accessed historical temporal sensor measurements of the plurality of control variables by removing outliers, removing short spikes and smoothing. 8. The computer implemented method of claim 1, wherein the learning algorithm is a Lasso model. 9. The computer implemented method of claim 1, further comprising generating the temporal causal dependency model for the first control variable of the plurality of control variables at the first station in the subsystem, based on the plurality of time series of sensor measurements of a plurality of variables at the first station, and temporal delay characteristics of the plurality of time series of sensor measurements of the plurality of variables at the stations directly connected to the first station. 10. A computer system for forecasting leaks in a fluid-delivery pipeline network, comprising: a memory; and a processor configured to: identify a subsystem in the pipeline network, the subsystem comprising a plurality of stations that are topologically connected; access historical temporal sensor measurements of a plurality of variables from a first station and from stations directly connected to the first station of the plurality of stations in the subsystem, the historical temporal sensor measurements comprising a plurality of time series of sensor measurements of the plurality of variables, the sensor measurements of each time series being captured at corresponding timestamps over a predetermined time-span; generate a temporal causal dependency model for a first control variable of the plurality of variables at the first station in the subsystem, based on the plurality of time series of sensor measurements of a second variable of the plurality of variables at the first station, and temporal delay characteristics of the plurality of time series of sensor measurements of the second variable at the stations directly connected to the first station; automatically calculate a normal operating value of the first control variable at the first station by applying a learning algorithm to train the temporal causal dependency model over a first training period comprising one or more time series of sensor measurements made under normal operating conditions; automatically calculate deviations between actual measured values of the first control variable at the first station and the normal operating value of the first control variable at the first station over the first training period; and determine a threshold deviation of the first control variable that indicates a leak event by calculating the deviations of the first control variable for a second training period comprising a plurality of time series of sensor measurements made under both normal operating conditions and leak event operating conditions. 11. A computer system of claim 10, wherein the plurality of time series of sensor measurements of a second variable of the plurality of variables at the first station are current values of the sensor measurements, and the temporal delay characteristics of the plurality of time series of sensor measurements of the second variable at the stations directly connected to the first station are lagged values of the sensor measurements. 12. A computer system of claim 10, wherein the processor is further configured to apply the learning algorithm to the temporal causal dependency model to compute a risk score of the first control variable, the risk score being based on differences between the calculated deviations and the threshold deviation, and the learning algorithm is a Lasso model. 13. A computer system of claim 10, wherein the historical temporal sensor measurements of the plurality of control variables are accessed from a data repository of a SCADA system monitoring the fluid-delivery pipeline network. 14. A computer system of claim 10, wherein the processor is further configured to process the accessed historical temporal sensor measurements of the plurality of control variables by removing outliers, removing short spikes and smoothing. 15. A computer system of claim 10, wherein the processor is further configured to generate the temporal causal dependency model for the first control variable of the plurality of control variables at the first station in the subsystem, based on the plurality of time series of sensor measurements of a plurality of variables at the first station, and temporal delay characteristics of the plurality of time series of sensor measurements of the plurality of variables at the stations directly connected to the first station. 16. A non-transitory article of manufacture tangibly embodying computer readable instructions, which when implemented, cause a computer to perform the steps of a method for forecasting leaks in a fluid-delivery pipeline network, comprising: identifying a subsystem in the pipeline network, the subsystem comprising a plurality of stations that are topologically connected; accessing historical temporal sensor measurements of a plurality of variables from a first station and from stations directly connected to the first station of the plurality of stations in the subsystem, the historical temporal sensor measurements comprising a plurality of time series of sensor measurements of the plurality of variables, the sensor measurements of each time series being captured at corresponding timestamps over a predetermined time-span; generating a temporal causal dependency model for a first control variable of the plurality of variables at the first station in the subsystem, based on the plurality of time series of sensor measurements of a second variable of the plurality of variables at the first station, and temporal delay characteristics of the plurality of time series of sensor measurements of the second variable at the stations directly connected to the first station; automatically calculating a normal operating value of the first control variable at the first station by applying a learning algorithm to train the temporal causal dependency model over a first training period comprising one or more time series of sensor measurements made under normal operating conditions; automatically calculating deviations between actual measured values of the first control variable at the first station and the normal operating value of the first control variable at the first station over the first training period; and determining a threshold deviation of the first control variable that indicates a leak event by calculating the deviations of the first control variable for a second training period comprising a plurality of time series of sensor measurements made under both normal operating conditions and leak event operating conditions. 17. A non-transitory article of manufacture of claim 16, wherein the plurality of time series of sensor measurements of a second variable of the plurality of variables at the first station are current values of the sensor measurements, and the temporal delay characteristics of the plurality of time series of sensor measurements of the second variable at the stations directly connected to the first station are lagged values of the sensor measurements. 18. A non-transitory article of manufacture of claim 16, further comprising applying the learning algorithm to the temporal causal dependency model to compute a risk score of the first control variable, the risk score being based on differences between the calculated deviations and the threshold deviation, and the learning algorithm is a Lasso model. 19. A non-transitory article of manufacture of claim 16, wherein the historical temporal sensor measurements of the plurality of control variables are accessed from a data repository of a SCADA system monitoring the fluid-delivery pipeline network, and further comprising processing the accessed historical temporal sensor measurements of the plurality of control variables by removing outliers, removing short spikes and smoothing. 20. A non-transitory article of manufacture of claim 16, further comprising generating the temporal causal dependency model for the first control variable of the plurality of control variables at the first station in the subsystem, based on the plurality of time series of sensor measurements of a plurality of variables at the first station, and temporal delay characteristics of the plurality of time series of sensor measurements of the plurality of variables at the stations directly connected to the first station.