Forecasting Nonlinear Nonstationary Processes in Machine Learning Task
Petro Ivanovich Bidyuk, Aleksandr Gozhyj, Irina Kalinina, Victoria Vysotska, Mikhail Vasilev, Romanna Malets · 2020
The article discusses the features of the solving the forecasting problems using machine learning techniques. The issues of accounting and correctly processing non-linear non-stationary processes in the problems of modeling and forecasting time series in various areas are considered. The analysis of stages and methods for solving machine learning problems. As an example, consider the problem of predicting currency pairs based on historical data. A comparative analysis of the normalization methods in data clustering is given. For the six currency pairs a short-term forecast is proposed.