Surrogate data
See also: Surrogate data testing
Surrogate data, sometimes known as analogous data,[1] usually refers to time series data that is produced using well-defined (linear) models like ARMA processes that reproduce various statistical properties like the autocorrelation structure of a measured data set.[2] The resulting surrogate data can then for example be used for testing for non-linear structure in the empirical data.
Surrogate or analogous data may refer to data used to supplement available data from which a mathematical model is built. Under this definition, it may be generated or transformed from another source.[1]
References
- 1 2 Kaefer, Paul E. (2015). Transforming Analogous Time Series Data to Improve Natural Gas Demand Forecast Accuracy (M.Sc. thesis). Marquette University. Retrieved 2016-02-18.
- ↑ Prichard; Theiler (1994). "Generating surrogate data for time series with several simultaneously measured variables" (PDF). Physical Review Letters. 73 (7): 951–954. doi:10.1103/physrevlett.73.951.
Further reading
- Schreiber, T.; Schmitz, A. (1996). "Improved Surrogate Data for Nonlinearity Tests". Physical Review Letters. 77 (4): 635–638. Bibcode:1996PhRvL..77..635S. doi:10.1103/PhysRevLett.77.635. PMID 10062864.
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