Creating artificial neural networks that generalize
Neural Networks
Training with noise is equivalent to Tikhonov regularization
Neural Computation
Neural network models for time series forecasts
Management Science
Ensemble learning via negative correlation
Neural Networks
A simulation study of artificial neural networks for nonlinear time-series forecasting
Computers and Operations Research
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
An investigation of neural networks for linear time-series forecasting
Computers and Operations Research
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Knowledge Engineering Review
Evolving RBF neural networks for time-series forecasting with EvRBF
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
A hybrid genetic-neural architecture for stock indexes forecasting
Information Sciences: an International Journal - Special issue: Computational intelligence in economics and finance
Time-series forecasting using flexible neural tree model
Information Sciences: an International Journal
Hybrid neural network models for hydrologic time series forecasting
Applied Soft Computing
Expert Systems with Applications: An International Journal
Forecasting nonlinear time series with neural network sieve bootstrap
Computational Statistics & Data Analysis
Information Sciences: an International Journal
Lowering variance of decisions by using artificial neural network portfolios
Neural Computation
A comparison between neural-network forecasting techniques-case study: river flow forecasting
IEEE Transactions on Neural Networks
Training neural networks with additive noise in the desired signal
IEEE Transactions on Neural Networks
A hybrid linear-neural model for time series forecasting
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Using additive noise in back-propagation training
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Neural modeling for time series: A statistical stepwise method for weight elimination
IEEE Transactions on Neural Networks
Time series forecasting with a non-linear model and the scatter search meta-heuristic
Information Sciences: an International Journal
A non-symbolic implementation of abdominal pain estimation in childhood
Information Sciences: an International Journal
Improving artificial neural networks' performance in seasonal time series forecasting
Information Sciences: an International Journal
Diversity of ability and cognitive style for group decision processes
Information Sciences: an International Journal
A Fuzzy Asymmetric GARCH model applied to stock markets
Information Sciences: an International Journal
An artificial neural network (p,d,q) model for timeseries forecasting
Expert Systems with Applications: An International Journal
Troika - An improved stacking schema for classification tasks
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Wind Speed Forecasting Using a Hybrid Neural-Evolutive Approach
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Distortion-free predictive streaming time-series matching
Information Sciences: an International Journal
A locally linear RBF network-based state-dependent AR model for nonlinear time series modeling
Information Sciences: an International Journal
Information Sciences: an International Journal
Reducing the search space in evolutive design of ARIMA and ANN models for time series prediction
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
A class of hybrid morphological perceptrons with application in time series forecasting
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
On the use of cross-validation for time series predictor evaluation
Information Sciences: an International Journal
Sparsely connected neural network-based time series forecasting
Information Sciences: an International Journal
Training regression ensembles by sequential target correction and resampling
Information Sciences: an International Journal
A global-local optimization approach to parameter estimation of RBF-type models
Information Sciences: an International Journal
Financial time series forecasting with a bio-inspired fuzzy model
Expert Systems with Applications: An International Journal
A meta learning approach to grammatical error correction
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Revenue forecasting using a least-squares support vector regression model in a fuzzy environment
Information Sciences: an International Journal
Load forecasting accuracy through combination of trimmed forecasts
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
Recentness biased learning for time series forecasting
Information Sciences: an International Journal
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Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing decision makers in many areas. Combining multiple models can be an effective way to improve forecasting performance. Recently, considerable research has been taken in neural network ensembles. Most of the work, however, is devoted to the classification type of problems. As time series problems are often more difficult to model due to issues such as autocorrelation and single realization at any particular time point, more research is needed in this area. In this paper, we propose a jittered ensemble method for time series forecasting and test its effectiveness with both simulated and real time series. The central idea of the jittered ensemble is adding noises to the input data and thus augments the original training data set to form models based on different but related training samples. Our results show that the proposed method is able to consistently outperform the single modeling approach with a variety of time series processes. We also find that relatively small ensemble sizes of 5 and 10 are quite effective in forecasting performance improvement.