IOTA

TOCSY - Toolboxes for Complex Systems: Inner composition alignment (IOTA) is a permutation-based association measure to detect regulatory links from very short time series. One time series is reodered with regards to the rank order of a second one and it monotonicity is evaluated.

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References in zbMATH (referenced in 10 articles )

Showing results 1 to 10 of 10.
Sorted by year (citations)

  1. Zhang, Yichi; Li, Yonggang; Deng, Wenfeng; Huang, Keke; Yang, Chunhua: Complex networks identification using Bayesian model with independent Laplace prior (2021)
  2. Cheng, Hong; Cai, David; Zhou, Douglas: The extended Granger causality analysis for Hodgkin-Huxley neuronal models (2020)
  3. Ma, Chuang; Chen, Han-Shuang; Li, Xiang; Lai, Ying-Cheng; Zhang, Hai-Feng: Data based reconstruction of duplex networks (2020)
  4. Haruna, Taichi: Partially ordered permutation complexity of coupled time series (2019)
  5. Li, Guangjun; Li, Na; Liu, Suhui; Wu, Xiaoqun: Compressive sensing-based topology identification of multilayer networks (2019)
  6. Han, Jingti; Mao, Changmei; Soto, Ricardo: Roles of clustering coefficient for the network reconstruction (2018)
  7. Bianco-Martinez, E.; Rubido, N.; Antonopoulos, Ch. G.; Baptista, M. S.: Successful network inference from time-series data using mutual information rate (2016)
  8. Wang, Wen-Xu; Lai, Ying-Cheng; Grebogi, Celso: Data based identification and prediction of nonlinear and complex dynamical systems (2016)
  9. Zhao, Xiaojun; Shang, Pengjian; Wang, Jing: Measuring the asymmetric contributions of individual subsystems (2014)
  10. Tang, Longkun; Lu, Jun-An; Wu, Xiaoqun; Lü, Jinhu: Impact of node dynamics parameters on topology identification of complex dynamical networks (2013)