Method for spatio-temporal monitoring

A system that determines how a service level experience (SLE) event in a communications environment should be addressed comprising:

• one or more hardware processors connected to a memory, the memory storing instructions that when executed configure the one or more hardware processors to perform operations comprising:
• determining, during each of a plurality of time periods, values of a plurality of SLE elements;
• constructing a time series of multi-dimensional SLE status vectors (SLESV, the constructing based on the values of the plurality of SLE element values determined during each of the plurality of time periods;
• constructing a second time series of multi-dimensional augmented service level experience status vectors (ASLESV), each dimension of the ASLESVs indicating, for each of the plurality of time periods, an SLE element value, a minimum value of the SLE element, and a maximum value of the SLE element, the minimum and maximum values based on SLE element values determined during time periods longer than each of the plurality of time periods;
• inputting the second time-series (MTS) to a predictive model;
• predicting, based on the inputting and the predictive model, an ASLESV, the ASLESV including predicted minimum and maximum values of each of the SLE elements included in the time series;
• initiating an action at least based on a determination of whether a measured SLESV parameter falls within a predicted minimum or maximum value of the SLESV parameter value of the predicted ASLESV.

A non-transitory computer readable storage medium comprising instructions that when executed configure hardware processing circuitry to perform operations to determine how a service level experience (SLE) event in a communications environment should be addressed, the operations comprising:

• determining, during each of a plurality of time periods, values of a plurality of SLE elements;
• constructing a time series of multi-dimensional SLE status vectors (SLESV), the constructing based on the values of the plurality of SLE element values determined during each of the plurality of time periods;
• constructing a second time series of multi-dimensional augmented service level experience status vectors (ASLESV), each dimension of the ASLESVs indicating, for each of the plurality of time periods, an SLE element value, a minimum value of the SLE element, and a maximum value of the SLE element, the minimum and maximum values based on SLE element values determined during time periods longer than each of the plurality of time periods;
• inputting the second time-series (MTS) to a predictive model;
• predicting, based on the inputting and the predictive model, an ASLESV, the ASLESV including predicted minimum and maximum values of each of the SLE elements included in the time series;
• initiating an action at least based on a determination of whether a measured SLESV parameter falls within a predicted minimum or maximum value of the SLESV parameter value of the predicted ASLESV.