Practical neural network recipes in C++
Practical neural network recipes in C++
On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
Finite impulse response neural networks with applications in time series prediction
Finite impulse response neural networks with applications in time series prediction
What are the implications of long-range dependence for VBR-video traffic engineering?
IEEE/ACM Transactions on Networking (TON)
Physica D
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
On the relevance of long-range dependence in network traffic
IEEE/ACM Transactions on Networking (TON)
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Field Guide to Dynamical Recurrent Networks
Field Guide to Dynamical Recurrent Networks
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Statistical properties of MPEG video traffic and their impact on traffic modeling in ATM systems
LCN '95 Proceedings of the 20th Annual IEEE Conference on Local Computer Networks
Nonlinear Time Series Analysis
Nonlinear Time Series Analysis
Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications (Advances in Industrial Control)
Methodology for long-term prediction of time series
Neurocomputing
A learning algorithm for continually running fully recurrent neural networks
Neural Computation
Prediction of MPEG-coded video source traffic using recurrent neural networks
IEEE Transactions on Signal Processing
Computational capabilities of recurrent NARX neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning long-term dependencies in NARX recurrent neural networks
IEEE Transactions on Neural Networks
A comparison between neural-network forecasting techniques-case study: river flow forecasting
IEEE Transactions on Neural Networks
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Sparse basis selection: new results and application to adaptive prediction of video source traffic
IEEE Transactions on Neural Networks
Dynamic neural-based buffer management for queuing systems with self-similar characteristics
IEEE Transactions on Neural Networks
Learning long-term dependencies with gradient descent is difficult
IEEE Transactions on Neural Networks
Gradient calculations for dynamic recurrent neural networks: a survey
IEEE Transactions on Neural Networks
Chaotic model with data assimilation using NARX network
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
WSEAS Transactions on Systems and Control
Learning from demonstration in robots: Experimental comparison of neural architectures
Robotics and Computer-Integrated Manufacturing
A Neural Network Scheme for Long-Term Forecasting of Chaotic Time Series
Neural Processing Letters
Biological time series segmentation using dynamic neural network model
Optical Memory and Neural Networks
Behavioural pattern identification and prediction in intelligent environments
Applied Soft Computing
Future Generation Computer Systems
| Hi-index | 0.01 |
The NARX network is a dynamical neural architecture commonly used for input-output modeling of nonlinear dynamical systems. When applied to time series prediction, the NARX network is designed as a feedforward time delay neural network (TDNN), i.e., without the feedback loop of delayed outputs, reducing substantially its predictive performance. In this paper, we show that the original architecture of the NARX network can be easily and efficiently applied to long-term (multi-step-ahead) prediction of univariate time series. We evaluate the proposed approach using two real-world data sets, namely the well-known chaotic laser time series and a variable bit rate (VBR) video traffic time series. All the results show that the proposed approach consistently outperforms standard neural network based predictors, such as the TDNN and Elman architectures.