Pattern Recognition in Non-Stationary Environmental Time Series Using Sparse Regression

The various real-world tasks of environmental management make it necessary to obtain the hindcasts and forecasts of natural events (wind, ocean waves and currents, sea ice, etc.) using data-driven techniques for metocean processes simulation. The models can be fitted to specific fragments of the non-stationary multivariate time series individually to reproduce metocean environment with desired characteristics. In the paper, the approach based on the LASSO regularised regression is proposed for the environmental time series clustering. It allows the identify the situations with specific interaction between variables, that can be interpreted by the values regression coefficients. The weather generator was used to produce both synthetic time series similar to the general dataset and the identified clusters. The obtained results can be used to increase the quality of the computationally lightweight environmental models’ identification and interpretation.