Improvement in global forecast for chaotic time series
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Abstract
In the Polynomial Global Approach to Time Series Analysis, the most costly (computationally speaking) step is the finding of the fitting polynomial. Here we present two routines that improve the forecasting. In the first, an algorithm that greatly improves this situation is introduced and implemented. The heart of this procedure is implemented on the specific routine which performs a mapping with great efficiency. In comparison with the similar procedure of the TimeS package developed by Carl...
Title of program: LinMapTS
Catalogue Id: AFAJ_v1_0
Nature of problem
Time series analysis and improving forecast capability.
Versions of this program held in the CPC repository in Mendeley Data
AFAJ_v1_0; LinMapTS; 10.1016/j.cpc.2016.05.011
This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018)
创建时间：
2019-11-11
