Nonlinear Time Series Analysis

2015

1,881 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c56fade1865b76b343323782162c2f451f415ef0

Citations in the corpus

• Highly Comparative Feature-Based Time-Series Classification (2014, background): “For example, estimating the correlation dimension of a time-delay embedded time series requires extremely long and precise recordings of a system [45].”
• Granger Causality: A Review and Recent Advances (2021, unknown): “In such cases, model-based methods have been shown to fail in numerous real-world settings (Ter¨asvirta et al., 2010; Tong, 2011; Lusch et al., 2016).”
• A Genetic Algorithm to Optimize Lazy Learning Parameters for the Prediction of Customer Demands (2013, methodology): “Common choices for the calculation of a delay time τL are the first zero of the autocorrelation function [13] or the first minimum of the average mutual information [16].”
• Time series forecasting for nonlinear and non-stationary processes: a review and comparative study (2015, background): “…portrait holds strong similarity to (strictly speaking, diffeomorphic [in the sense that there is a smooth function that maps all points in the underlying state space to reconstructed state space, and vice versa] to the observable subset of) the original state space (Kantz and Schreiber, 1997).”
• A Review of Nonlinear Hyperspectral Unmixing Methods (2014, unknown): “One introduces a statistical test based on surrogate data [198]: The null-hypothesis is that the data is distributed according to an underlying linear statistical process, a surrogate data set is constructed with the same statistical properties as the”
• Complex dynamical systems in social and personality psychology: theory, modeling, and analysis (2014, unknown): “…of several common and generally applicable linear and nonlinear time-series methods for research in socialpersonality psychology (for more detailed discussion and tutorials, see Abarbanel, 1996; Boker & Wenger, 2007; Gottman, 1981; Heath, 2000; Kantz & Schreiber 1997; Riley & Van Orden, 2005).”
• Atomic Norm Denoising With Applications to Line Spectral Estimation (2012, methodology): “Cadzow’s technique is very similar [41] to a popular technique in time series literature [42], [43] called Singular Spectrum Analysis [44], which uses autocorrelation matrix in-”
• Nonlinear time-series analysis revisited. (2015, background): “Readers who want to enter this subject more deeply should consult one of the many comprehensive review articles or useful monographs on this topic, such as 4,5 .”
• Meta-learning with neural networks and landmarking for forecasting model selection an empirical evaluation of different feature sets applied to industry data (2016, methodology): “In general, for performing an RQA, it is recommended to compute m by the FNN-algorithm and τ as the first minimum of the average mutual information (AMI) [22], [32].”
• Complex network approaches to nonlinear time series analysis (2019, methodology): “Building network models for time series prediction Time series modeling and forecasting has attracted a great number of researchers’ attention and provides the core of nonlinear time series analysis [6,17].”
• Neural Granger Causality (2021, background): “Model-based methods may fail in real world cases when the relationships between the past of one series and future of another falls outside of the model class [18], [19], [20].”
• A Bias and Variance Analysis for Multistep-Ahead Time Series Forecasting (2016, unknown): “There are many situations where the time series behaves nonlinearly, and therefore a nonlinear model would be the appropriate choice [16].”
• The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives (2024, methodology): “…bi-and multivariate time-series-analysis techniques grounded in statistics (Rodgers and Nicewander, 1988; Hamilton, 2020), nonlinear dynamics (Kantz and Schreiber, 2003; Datseris and Parlitz, 2022), synchronization theory (Arnhold et al., 1999; Pikovsky et al., 2001; Stankovski et al., 2012;…”