SMOOTHING INCOMPLETE DATA SERIES OF ENVIRONMENTAL MONITORING STATIONS USING PREDICTIVE MODELS
Keywords:
environmental monitoring, data series, data series dynamics, data gaps, forecast, predictive modelsAbstract
The article examines the problem of incomplete data series in the analysis of data from environmental monitoring stations, its impact on the reliability of the obtained results and the prognostic suitability of input data, as well as data smoothing methods that allow minimizing the negative impact of data gaps. The absence of data in the series can manifest itself in practice as false zero values that can lead to significant deviations, as well as missing data, which in some cases hides trends in the dynamics of the series. At the same time, the analyst may not be aware of the presence of empty or null values, which, as a result, leads to false conclusions or predictions. Smoothing methods using a simple moving average and extrapolation allow to improve the quality of input data, and, as a result, to improve the predictive quality of the obtained predictive models. Using local forecasts to fill in missing values allows you to get the most accurate results instead of missing data and, as a result, improve the predictive quality of the developed forecast models. The accuracy of the results obtained instead of missing data is checked by calculating the main statistical indicators of the series with empty values and the complete series. Calculation of the parameters of forecasting models to fill the empty intervals can be based on previous data or the trend of the entire series. The results obtained in this study can be used in the future to fill incomplete series in the analysis of data from environmental monitoring stations or other series of data used for forecasting or analytical calculations.
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