Time series and dependent variables
Physica D
Simplifying neural networks by soft weight-sharing
Neural Computation
A New Intelligent System Methodology for Time Series Forecasting with Artificial Neural Networks
Neural Processing Letters
Predictive delay metric for OLSR using neural networks
WICON '07 Proceedings of the 3rd international conference on Wireless internet
RCGA-S/RCGA-SP Methods to Minimize the Delta Test for Regression Tasks
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Non-parametric residual variance estimation in supervised learning
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Minimising the delta test for variable selection in regression problems
International Journal of High Performance Systems Architecture
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
A class of hybrid morphological perceptrons with application in time series forecasting
Knowledge-Based Systems
Variable selection in a GPU cluster using delta test
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
A robust automatic phase-adjustment method for financial forecasting
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Regressor selection with the analysis of variance method
Automatica (Journal of IFAC)
A Morphological-Rank-Linear evolutionary method for stock market prediction
Information Sciences: an International Journal
Evolutionary Learning Processes to Design the Dilation-Erosion Perceptron for Weather Forecasting
Neural Processing Letters
Parametric and non-parametric feature selection for kidney transplants
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
Environmental Modelling & Software
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We present a general method, the δ-test, which establishes functional dependencies given a sequence of measurements. The approach is based on calculating conditional probabilities from vector component distances. Imposing the requirement of continuity of the underlying function, the obtained values of the conditional probabilities carry information on the embedding dimension and variable dependencies. The power of the method is illustrated on synthetic time-series with different time-lag dependencies and noise levels and on the sunspot data. The virtue of the method for preprocessing data in the context of feedforward neural networks is demonstrated. Also, its applicability for tracking residual errors in output units is stressed.