Based on Manhattan distance the classification scheme of correlation between time series is proposed.The method allows to measure the strength of dependence beteen time series as a order of polynomial fitted to the cumulated Manhattan distance between time series. Moreover, the methods allows to measure stability of correlation by estimating the quality ofthe fit appropriate curves. The method is illustrated on the example of the correlation analysis of GDP per capita for 19 of the most developed countries in the world. The correlations were analysed in the short, medium and long time distance correlation rrage (10, 20 and 30 years respectively). The obtained distance matrices were used to construct network structures assuming that the link beteen countries exists if the distance between them is greater than given eference values ​​(median, mean and the mean minus and plus standard deviation). Obtained network are than discussed. The results were compared with analysis based on the ultrametric correlation distance. 

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