Gradient radial basis function networks for nonlinear and nonstationary time series prediction
Application of chaos and fractal models to water quality time series prediction
Tang, "An EMD-SVR method for non-stationary time series prediction
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A New Algorithm for Time Series Prediction
by Temporal Fuzzy Clustering.
Time series prediction
can be seen as the task of finding regularities and dependencies in the data set, and NNs can be taught to emulate the underlying dynamics of the system.
We distinguished three types' economic applications of neural networks: Classification of economic agents, time series prediction
and the modeling of bounded rational agents.
But the chaotic time series prediction
studied in this paper has a completely different mechanism.
This builds an internal memory for time series prediction
For this purpose, many data-driven models have been developed, including linear, nonlinear, parametric and nonparametric models for hydrologic time series prediction
in the past decades (Marques et al.
At present, the mainstream prediction methods adopted by researchers and enterprises in the industry on supply chain demand prediction are roughly categorized as two categories: one is single prediction method, such as adopting neural network prediction method, grey prediction method, markov prediction method, time series prediction
method, prediction method based on quantity of value; the other is combination prediction method, i.
The paper deals with the utilization of time series prediction
for control of technological process in real time.
A variety of traditional time series prediction
approaches have already been proposed for this problem, such as fuzzy rule , Kalman filter , grey prediction , ARMA , and multiple regression .