Iterative Algorithm for Time Series Decomposition into Trend and Seasonality: Testing Using the Example of CO2 Concentrations in the Atmosphere

These series have a significant trend with clearly expressed seasonal variations changing from year to year and a sufficient length of observations.

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This algorithm is based on the approaches proposed by the authors in several previous studies and allows unbiased estimates for the trend and seasonal components for data with a strong trend containing different periodic (including seasonal) variations, as well as observational gaps and omissions.

also in 5,725 other papers