авторегресійна модель

This paper explores multifractal analysis for the selected time series water pollution data set and further prediction based on BOD measure with ARFIMA-based fractal model. MFDFA multifractal algorithm is applied for estimating the fractal differentiation parameter of the ARFIMA. The obtained results are compared with similar obtained with autoregressive ARIMA model and basic ARFIMA fractal model. The study reveals an enhancement in accuracy with the use of combination of multifractal analysis and fractal methods for water pollution prediction

This paper explores the application of the ARFIMA fractal model for prediction of the dynamics of river water pollution based on BOD measure. The study begins by conducting a review of related works in the field of water quality analysis. At this stage also a suitable dataset is selected, that is used to train the ARFIMA, one of the machine learning models. GPH semi-parametric algorithm is applied for estimating the fractal differentiation parameter of the ARFIMA.