Abstract
The problem of prediction in chaotic environments based on identifying analog situations in arrays of retrospective data are considered. Traditional recognition schemes are ineffective and form weak classifiers in cases where the system component of the observed process is represented by a non-periodic oscillatory time series (realization of chaotic dynamics). The objective is to develop a system of such classifiers, which allows for improvements in the quality of forecasts for non-stationary dynamics in flow processes. The introduced technique can be applied for the prediction of oscillatory non-periodic processes with non-stationary noise, i.e., dependence of different relay frequencies, external electric potential and microchannel width in an electrokinetic micromixer.
Highlights
Forecasting of nonstationary processes plays an important role in different areas such as weather forecasting or prediction in economics
The classical statistical approaches are based on generative models such as AutoRegressive-Moving Average (ARMA) and AutoRegressive Integrated Moving Average (ARIMA) [1,2]
The vertical red line marks the zone of retrospective data used to generate a precedent prediction from the working zone on which the algorithm is tested
Summary
Forecasting of nonstationary processes plays an important role in different areas such as weather forecasting or prediction in economics. Prediction of chemical processes has continued to arouse the interest of the research community in recent years. Forecasting of these processes can enable shorter downtimes and more effective maintenance of setups, which results in higher operation reliability and lower costs. Depending on the applied methods and features of the process, different routes for forecasting are possible. The classical statistical approaches are based on generative models such as AutoRegressive-Moving Average (ARMA) and AutoRegressive Integrated Moving Average (ARIMA) [1,2]. These models need strict assumptions concerning noise of the investigated process
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