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pro vyhledávání: '"Sudhakar Kathari"'
Autor:
Sudhakar Kathari, Arun K. Tangirala
Publikováno v:
Industrial & Engineering Chemistry Research. 61:18426-18444
Scalar correlation functions for model structure selection in high-dimensional time-series modelling
Autor:
Sudhakar Kathari, Arun K. Tangirala
Publikováno v:
ISA Transactions. 100:275-288
Model structure selection is an important step in high-dimensional time-series modelling. Traditionally AIC and BIC have been used for this purpose, however, only post model estimation. On the other hand, modern approaches use penalized regression me
Autor:
Sudhakar Kathari, Arun K. Tangirala
Publikováno v:
Industrial & Engineering Chemistry Research. 58:11275-11294
Multivariable dynamical processes are characterized by complex cause and effect relationships among variables. Reconstruction of these causal connections from data, especially based on the concept of Granger causality (GC), has attracted significant
Autor:
Vivek Shankar Pinnamaraju, Bala Shyamala Balaji, Hemanth Kumar Tanneru, Satheesh Kumar Perepu, Sudhakar Kathari
Publikováno v:
Advances in Intelligent Systems and Computing ISBN: 9783030731021
Ensemble models are powerful model building tools that are developed with a focus to improve the accuracy of model predictions. They find applications in time series forecasting in varied scenarios including but not limited to process industries, hea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c2dbe2afbb44ff76cb58acb25234753f
https://doi.org/10.1007/978-3-030-73103-8_43
https://doi.org/10.1007/978-3-030-73103-8_43
Autor:
Sudhakar Kathari, Arun K. Tangirala
Publikováno v:
SICE
Reconstruction of process topology from cause-effect analysis of measurements finds applications in root-cause analysis, identification of disturbance propagation pathways, estimation of fault propagation times, and interaction assessment. A widely u
Autor:
Arun K. Tangirala, Sudhakar Kathari
Publikováno v:
IFAC-PapersOnLine. 49:77-82
Identification of network structure and quantifying the connectivity strengths in multivariate systems is an important problem in many scientific areas. Data-driven approach to network reconstruction based on causality measures is an emerging field o