The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Nov. 05, 2019
Filed:
Sep. 21, 2016
Tata Consultancy Services Limited, Mumbai, IN;
Kriti Kumar, Whitefiled, IN;
Naveen Kumar Thokala, Whitefiled, IN;
Ravikumar Karumanchi, Whitefiled, IN;
Mariswamy Girish Chandra, Whitefiled, IN;
Kalyan Prathap Kamakolanu Guru, Bangalore, IN;
Suresh Upparapalli, Bangalore, IN;
Madhusudhan Kamma Chavala Chowdary, Hyderabad, IN;
Prasanna Madhavrao Kulkarni, Bangalore, IN;
Pareshkumar Bhawanishankar Sharda, Houston, TX (US);
Tata Consultancy Services Limited, Mumbai, IN;
Abstract
System and method for predicting enterprise system response time is disclosed. System pre-processes causal variables of historical output time series data to select subset of causal variables by applying regression techniques to obtain significant causal variables. Historical output time series data shows response time of enterprise system. System derives dummy variables from historical output time series data using threshold based method. Dummy variables are specific to peak detection and trough detection in historic output time series data. System trains predictive model using historical output time series data, significant causal variables, and dummy variables to generate trained predictive model and predictive model designed using machine learning technique selected based on forecast methodology used for forecasting input time series data. System predicts enterprise system response time by using trained predictive model, input time series data or lag between input time series data and historical output time series data.
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