Improvement in global forecast for chaotic time series

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)