Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Nonstationarity and Data Preprocessing for Neural Network Predictions of an Economic Time Series
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5 - Volume 5
Analysis of the predictive ability of time delay neural networksapplied to the S&P 500 time series
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Time-delay neural networks: representation and induction of finite-state machines
IEEE Transactions on Neural Networks
Neural modeling for time series: A statistical stepwise method for weight elimination
IEEE Transactions on Neural Networks
An artificial neural network (p,d,q) model for timeseries forecasting
Expert Systems with Applications: An International Journal
A neural network-based fuzzy time series model to improve forecasting
Expert Systems with Applications: An International Journal
Ensembles of neural networks with generalization capabilities for vehicle fault diagnostics
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Simulating wheat yield in New South Wales of Australia using interpolation and neural networks
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
A new hybrid methodology for nonlinear time series forecasting
Modelling and Simulation in Engineering
A new class of hybrid models for time series forecasting
Expert Systems with Applications: An International Journal
Correcting and combining time series forecasters
Neural Networks
Fuzzy artificial neural network p, d, q model for incomplete financial time series forecasting
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Many researchers have argued that combining many models for forecasting gives better estimates than single time series models. For example, a hybrid architecture comprising an autoregressive integrated moving average model (ARIMA) and a neural network is a well-known technique that has recently been shown to give better forecasts by taking advantage of each model's capabilities. However, this assumption carries the danger of underestimating the relationship between the model's linear and non-linear components, particularly by assuming that individual forecasting techniques are appropriate, say, for modeling the residuals. In this paper, we show that such combinations do not necessarily outperform individual forecasts. On the contrary, we show that the combined forecast can underperform significantly compared to its constituents' performances. We demonstrate this using nine data sets, autoregressive linear and time-delay neural network models.