Seasonal exponential smoothing with damped trends
Management Science
Time series forecasting using neural networks
APL '94 Proceedings of the international conference on APL : the language and its applications: the language and its applications
Neural network models for time series forecasts
Management Science
Using Feature Construction to Improve the Performance of Neural Networks
Management Science
Model selection in neural networks
Neural Networks
Forecasting exchange rates using general regression neural networks
Computers and Operations Research - Neural networks in business
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Neural Networks in the Capital Markets
Neural Networks in the Capital Markets
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Financial Forecasting
Neural Networks for Financial Forecasting
Neural Network Time Series Forecasting of Financial Markets
Neural Network Time Series Forecasting of Financial Markets
Advanced Methods in Neural Computing
Advanced Methods in Neural Computing
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
A general regression neural network
IEEE Transactions on Neural Networks
Neural modeling for time series: A statistical stepwise method for weight elimination
IEEE Transactions on Neural Networks
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A hybrid forecasting method is proposed which leverages from statistical and neural network techniques to perform multi-step ahead forecasting. The proposed method is based on the disaggregation of time series components, the prediction of each component individually and the reassembling of the extrapolations to obtain an estimation for the global data. The STL decomposition procedure from the literature [5] is implemented to obtain the seasonal, trend and irregular components of the time series, whilst Generalized Regression Neural Networks (GRNN) [12] are used to perform out-of sample extrapolations of the seasonal and residual components. The univariate Theta model is employed for the estimation of the directional component. The application of the GRNN is based on the dynamic calibration of the training process for each of the seasonal and irregular components individually. The proposed hybrid forecasting method is applied to 60 time series from the NN3 competition and 227 time series from the M1 Competition dataset, to obtain 18 out-of sample predictions. The results from the application demonstrate that the proposed method can outperform standard statistical techniques in the literature. One of the main contributions of the current research lies in the investigation of the strengths and weaknesses of the GRNN in extrapolating structural components of time series.