Geophysical processes and biosphere: article

Аnnotation: An iterative algorithm for decomposition of data series into trend and residual (including the seasonal effect) components is proposed. This algorithm is based on the approaches, proposed by the authors in several previous works, and allows the researcher to obtain non-biased estimates of the trend and seasonal components for data with a strong trend, containing various periodic variations, including seasonal, as well as gaps and missing observations. The main idea of the algorithm is that both the trend and the seasonal components should be estimated using the signal that is maximally cleaned of any other variations that are considered as interference, when assessing the trend component, seasonal variation is a hindrance, and vice versa. The iterative approach allows the researcher to be naturally included in the optimization procedure for models of both trend and seasonal components. The approximation procedure provides maximum flexibility and is fully controllable at all stages of the process. In addition, it allows you to naturally solve the problems that arise in the presence of missed observations and defective measurements, without filling such dates with artificially simulated values. The algorithm was tested on the example of data on changes in the concentration of CO