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[Licence| Download | New Version Template] afaj_v1_0.tar.gz(218 Kbytes)
Manuscript Title: Improvement in global forecast for chaotic time series
Authors: P.R.L. Alves, L.G.S. Duarte, L.A.C.P. da Mota
Program title: LinMapTS
Catalogue identifier: AFAJ_v1_0
Distribution format: tar.gz
Journal reference: Comput. Phys. Commun. 207(2016)325
Programming language: Maple 16.
Computer: Any capable of running Maple.
Operating system: Any capable of running Maple. Tested on Windows ME, Windows XP, Windows 7.
RAM: 128 MB bytes
Keywords: Time Series Analysis, Global Fitting, Predictability, Chaos, Symbolic Computation.
PACS: 05.45.Tp.
Classification: 4.3, 4.9, 5.

Nature of problem:
Time series analysis and improving forecast capability.

Solution method:
The basis of the solution method is the result published in [1].

Global variables X [i] are used in the generated map; If more than 2,000 vectors are employed in the global mapping the normality test is not applicable.

Unusual features:
When many polynomial coefficients are calculated (e.g., 55) their values can be different in distinct computers. These discrepancies do not affect significantly the accuracy in forecasting.

Running time:
A few seconds are required for the usual applications.

[1] H. Carli, L. Duarte, L. da Mota, A maple package for improved global mapping forecast, Comp. Phts. Comm. 185(2014)1115