Description: A framework to infer causality on a pair of time series of real numbers based on variable-lag Granger causality and transfer entropy. Typically, Granger causality and transfer entropy have an assumption of a fixed and constant time delay between the cause and effect. However, for a non-stationary time series, this assumption is not true. For example, considering two time series of velocity of person A and person B where B follows A. At some time, B stops tying his shoes, then running to catch up A. The fixed-lag assumption is not true in this case. We propose a framework that allows variable-lags between cause and effect in Granger causality and transfer entropy to allow them to deal with variable-lag non-stationary time series. Please see Chainarong Amornbunchornvej, Elena Zheleva, and Tanya Berger-Wolf (2019) <arXiv:1912.10829> when referring to this package in publications.
Packaged: 2020-05-17 09:36:28 UTC; Chainarong

Packaged: 2021-03-29 06:10:50 UTC; Chainarong

Description: A framework to infer causality on a pair of time series of real numbers based on variable-lag Granger causality and transfer entropy. Typically, Granger causality and transfer entropy have an assumption of a fixed and constant time delay between the cause and effect. However, for a non-stationary time series, this assumption is not true. For example, considering two time series of velocity of person A and person B where B follows A. At some time, B stops tying his shoes, then running to catch up A. The fixed-lag assumption is not true in this case. We propose a framework that allows variable-lags between cause and effect in Granger causality and transfer entropy to allow them to deal with variable-lag non-stationary time series. Please see Chainarong Amornbunchornvej, Elena Zheleva, and Tanya Berger-Wolf (2021) <doi:10.1145/3441452> when referring to this package in publications.

Title: Variational Mode Decomposition Based Machine Learning Models
Authors@R: c(person("Pankaj", "Das", role = c("aut","cre"),email="pankaj.das2@icar.gov.in"),
person("Girish", "Kumar Jha", role = "aut",email="grish.stat@gmail.com"),
person("Tauqueer", "Ahmad", role = "aut",email="Tauqueer.Ahmad@icar.gov.in"),
person("Achal", "Lama", role = "aut",email="achal.lama@icar.gov.in"),
person("Lampros", "Mouselimis", email = "mouselimislampros@gmail.com", role = "cph"))
Description: Application of Variational Mode Decomposition based different Machine Learning models for univariate time series forecasting. For method details see (i) K. Dragomiretskiy and D. Zosso (2014) <doi:10.1109/TSP.2013.2288675>; (ii)  Pankaj Das (2020) <http://krishi.icar.gov.in/jspui/handle/123456789/44138>.