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Time Series and the Forecasting Problem
Time Series Forecasting Using Neural Networks vs BoxJenkins Methodology
Application to Time Series
4 other sections not shown
activation functions adaptive networks algorithm application approach approximation architecture Artificial Neural Networks attractor back-propagation behavior Box-Jenkins model chaotic time series CNLS-net connectionist data points data set differential equation dimension dimensional dynamical systems Edited error curve example extrapolation accuracy Farber Figure filter Gaussian hidden layer hidden units input units input vector interpolation ISBN iterated Lapedes learning linear load forecasting logistic logistic map Mackey-Glass equation method modified PNN multi-lag multivariate neural network model neurons nodes noise nonlinear number of inputs obtained one-lag optimal oscillations output units Parallel Distributed Processing parameters performance phase space prediction error problem procedure propagation radial basis function random RMS error Rumelhart samples series analysis series forecasting series prediction sigmoid function sigmoidal signal Simulation squared errors statistical subnetwork techniques term test set Tiao time-series tion training data training set trajectories tree University values variables waveform weight-elimination weights zero