Review Articles

Nonlinear prediction via Hermite transformation

Tucker McElroy ,

a U.S. Census Bureau, Washington, DC, USA

Srinjoy Das

b University of California San Diego, La Jolla, CA, USA

Pages 49-54 | Received 25 Oct. 2019, Accepted 24 Nov. 2020, Published online: 17 Dec. 2020,
  • Abstract
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  • References
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General prediction formulas involving Hermite polynomials are developed for time series expressed as a transformation of a Gaussian process. The prediction gains over linear predictors are examined numerically, demonstrating the improvement of nonlinear prediction.


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