Employing Discrete Wavelet Transform Methods and Python Libraries to Derive Mathematical Models Of Environmental Data
The article addresses the urgent problem of computer modeling of large-scale environmental monitoring datasets using discrete wavelet transforms. The research object consists of time series of harmful pollutant concentrations in the atmosphere, including nitrogen oxides, benzene, and sulfur dioxide, collected from automated stations in Central and Eastern Europe. The input data are characterized by high stochasticity, noise, missing values, and temporal shifts, which significantly com- plicate the extraction of trends and patterns required for forecasting.