ARFIMA

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.

The work created software and algorithmic support for modeling and forecasting the Bitcoin cryptocurrency using the ARFIMA (AutoRegressive Fractionally Integrated Moving Average) fractal model. Time series forecasting models (autoregressive, fractal) were analyzed. The selection of the most appropriate parameters of the selected fractal model was also carried out to maximize accuracy in view of the RMSE metric. The series were analyzed for trend, seasonality, white noise, non-stationarity and long-term memory.