Patent ID: 11935077
Assignee: VUNET SYSTEMS PRIVATE LIMITED
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 16:
17. A non-transitory computer readable storage medium having embodied thereon, computer program codes comprising instructions executable by at least one processor for real-time operational predictive scoring of components and services of an information technology system for forecasting and assessing performance of said components of said information technology system, the instructions when executed by the at least one processor cause the processor to perform a method comprising:
collecting, in real-time, a plurality of time series signals comprising multiple metrics corresponding to one or more of health, performance, and functionality of each of said components of said information technology system;
receiving a data stream comprising said collected plurality of time series signals, and storing said received data stream as individual time series data;
generating a probabilistic forecast for each of said time series signals of said components of said information technology system collected in real-time, using a plurality of customized machine learning models comprising autoregressive Recurrent Neural Network based forecasting models, wherein said autoregressive Recurrent Neural Network based forecasting models serve as global models for multiple related time series signals and use past time series data of a fixed length and generate time series data in terms of probability distributions for a future time window of a prediction length;
training said customized machine learning as said global models for said multiple related time series signals based on a periodic schedule, wherein said training process is controlled through a cost and error calculation;
calculating an operational predictive score for each of said time series signals of said components of said information technology system as an indicator of performance of said each of said time series signals of said components of said information technology system in a near future, corresponding to said individual time series data of each of said plurality of time series signals, wherein said calculation comprises using a statistical model on said probabilistic forecast for each of said time series signals of said components of said information technology system;
aggregating said calculated operational predictive score of each of said time series signals of said components of said information technology system into an operational predictive score for each of said components of said information technology system as an indicator of performance of said components in said near future; and
automatically triggering retraining of said customized machine learning models based on monitoring a quality and an effectiveness of said operational predictive score of each of said time series signals and said components, wherein said monitoring said quality and said effectiveness of said operational predictive score of each of said time series signals and said components is based on factors comprising:
missed correlation between user experience and said operational predictive score for each of said time series signals of said components of said information technology system;
incidents without correlated operational predictive score alerts;
low user score; and
high forecasting error.