Surrogate data

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; this is called surrogate data testing.

Surrogate or analogous data also refers to data used to supplement available data from which a mathematical model is built. Under this definition, it may be generated (i.e., synthetic data) or transformed from another source.[1]

  1. ^ a b Kaefer, Paul E. (2015). Transforming Analogous Time Series Data to Improve Natural Gas Demand Forecast Accuracy (M.Sc. thesis). Marquette University. Archived from the original on 2016-03-12. Retrieved 2016-02-18.
  2. ^ Prichard; Theiler (1994). "Generating surrogate data for time series with several simultaneously measured variables" (PDF). Physical Review Letters. 73 (7): 951–954. arXiv:comp-gas/9405002. Bibcode:1994PhRvL..73..951P. doi:10.1103/physrevlett.73.951. PMID 10057582. S2CID 32748996.