Notes on detecting non-stationarity in stochastic processes/random fields.
Related: ensuring stability in a stochastic process. If we have a system with stable dynamics and keep the distribution of the inputs the same, then it will end up stationary.
Shay Palachy, Detecting stationarity in time series data.
Change point methods
See change points.
Nonparametric detection of changepoints
Spectral methods
References
Adams, Ryan Prescott, and David J. C. MacKay. 2007. “Bayesian Online Changepoint Detection.” arXiv:0710.3742 [Stat], October.
Aue, Alexander, and Anne van Delft. 2020. “Testing for Stationarity of Functional Time Series in the Frequency Domain.” The Annals of Statistics 48 (5).
Bagchi, Pramita, Vaidotas Characiejus, and Holger Dette. 2017. “A Simple Test for White Noise in Functional Time Series.” arXiv:1612.04996 [Math, Stat], September.
Berkes, István, Robertas Gabrys, Lajos Horváth, and Piotr Kokoszka. 2009. “Detecting Changes in the Mean of Functional Observations.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 71 (5): 927–46.
Borgnat, Pierre, Patrick Flandrin, Paul Honeine, Cédric Richard, and Jun Xiao. 2010. “Testing Stationarity With Surrogates: A Time-Frequency Approach.” IEEE Transactions on Signal Processing 58 (7): 3459–70.
Delft, Anne van, Vaidotas Characiejus, and Holger Dette. 2021. “A Nonparametric Test for Stationarity in Functional Time Series.” Statistica Sinica.
Delft, Anne van, and Michael Eichler. 2018. “Locally Stationary Functional Time Series.” Electronic Journal of Statistics 12 (1).
Dette, Holger, Philip Preuß, and Mathias Vetter. 2011. “A Measure of Stationarity in Locally Stationary Processes With Applications to Testing.” Journal of the American Statistical Association 106 (495): 1113–24.
Hegger, Rainer, Holger Kantz, Lorenzo Matassini, and Thomas Schreiber. 2000. “Coping with Nonstationarity by Overembedding.” Phys. Rev. Lett. 84 (18): 4092–95.
Livan, Giacomo, Jun-ichi Inoue, and Enrico Scalas. 2012. “On the Non-Stationarity of Financial Time Series: Impact on Optimal Portfolio Selection.” Journal of Statistical Mechanics: Theory and Experiment 2012 (07): P07025.
Paparoditis, Efstathios. 2010. “Validating Stationarity Assumptions in Time Series Analysis by Rolling Local Periodograms.” Journal of the American Statistical Association 105 (490): 839–51.
Preuß, Philip, Mathias Vetter, and Holger Dette. 2013. “A Test for Stationarity Based on Empirical Processes.” Bernoulli 19 (5B): 2715–49.
Saatçi, Yunus, Ryan Turner, and Carl Edward Rasmussen. 2010. “Gaussian Process Change Point Models.” In Proceedings of the 27th International Conference on International Conference on Machine Learning, 927–34. ICML’10. Madison, WI, USA: Omnipress.
Shalizi, Cosma Rohilla, Abigail Z Jacobs, and Aaron Clauset. n.d. “Adapting to Non-Stationarity with Growing Expert Ensembles.”
Shinohara, Shuji, Nobuhito Manome, Kouta Suzuki, Ung-il Chung, Tatsuji Takahashi, Hiroshi Okamoto, Yukio Pegio Gunji, Yoshihiro Nakajima, and Shunji Mitsuyoshi. 2020. “A New Method of Bayesian Causal Inference in Non-Stationary Environments.” PLOS ONE 15 (5): e0233559.
Thorisson, Hermann. 2000. Coupling, Stationarity, and Regeneration. Springer New York.
Vogt, Michael, and Holger Dette. 2015. “Detecting Gradual Changes in Locally Stationary Processes.” The Annals of Statistics 43 (2): 713–40.
Von Sachs, Rainer, and Michael H. Neumann. 2000. “A Wavelet-Based Test for Stationarity.” Journal of Time Series Analysis 21 (5): 597–613.
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