Abstract
Two novel codes for the prediction of time series are
presented. Unlike most of the prominent codes based on
finding a process that predicts the future data, these
codes are based on function analysis and symbolic
regression. Both codes are based on a generalization
and combination of series expansions, parameter
optimization techniques, and genetic programming. These
highly complex codes are outlined and applied to
different examples of physics and economy.
- algorithms,
- analysis,
- codes,
- complex
- computation,
- data,
- economy
- evolutionary
- expansions,
- function
- future
- generalized
- genetic
- highly
- hybrid
- optimization
- parameter
- physics,
- prediction,
- programming,
- prominent
- regression,
- series
- series,
- symbolic
- techniques,
- time
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