Sales forecasting using time series and neural networks
CIE '96 Proceedings of the 19th international conference on Computers and industrial engineering
Multiple neural networks for a long term time series forecast
Neural Computing and Applications
IEEE Transactions on Neural Networks
A comparison between neural-network forecasting techniques-case study: river flow forecasting
IEEE Transactions on Neural Networks
Weather analysis using ensemble of connectionist learning paradigms
Applied Soft Computing
A neural network ensemble method with jittered training data for time series forecasting
Information Sciences: an International Journal
Advances in Engineering Software
Expert Systems with Applications: An International Journal
Estimation of Rock Mass Rating System with an Artificial Neural Network
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
An artificial neural network (p,d,q) model for timeseries forecasting
Expert Systems with Applications: An International Journal
Credit rating by hybrid machine learning techniques
Applied Soft Computing
Forecasting of basin sediment yield based on wavelet-BP neural network
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 1
Structural damage detection using fuzzy cognitive maps and Hebbian learning
Applied Soft Computing
International Journal of Applied Evolutionary Computation
A new linear & nonlinear artificial neural network model for time series forecasting
Decision Support Systems
Forecasting conditional climate-change using a hybrid approach
Environmental Modelling & Software
| Hi-index | 0.00 |
The need for increased accuracies in time series forecasting has motivated the researchers to develop innovative models. In this paper, a new hybrid time series neural network model is proposed that is capable of exploiting the strengths of traditional time series approaches and artificial neural networks (ANNs). The proposed approach consists of an overall modelling framework, which is a combination of the conventional and ANN techniques. The steps involved in the time series analysis, e.g. de-trending and de-seasonalisation, can be carried out before gradually presenting the modified time series data to the ANN. The proposed hybrid approach for time series forecasting is tested using the monthly streamflow data at Colorado River at Lees Ferry, USA. Specifically, results from four time series models of auto-regressive (AR) type and four ANN models are presented. The results obtained in this study suggest that the approach of combining the strengths of the conventional and ANN techniques provides a robust modelling framework capable of capturing the non-linear nature of the complex time series and thus producing more accurate forecasts. Although the proposed hybrid neural network models are applied in hydrology in this study, they have tremendous scope for application in a wide range of areas for achieving increased accuracies in time series forecasting.