Minimum description length neural networks for time series prediction
نویسندگان
چکیده
منابع مشابه
A Temporal Minimum Description Length Policy for Evolving Neural Networks
One of the most important issues for computational methods is their time complexity. This paper introduces a temporal MDL (minimum description length) policy for evolving neural networks based on their execution time on the hosting hardware. Temporal MDL implements an adaptive selection pressure based on the actual processing time of the evolving solutions and thus favors creation of faster, mo...
متن کاملRecurrent neural networks for time-series prediction
Recurrent neural networks have been used for time-series prediction with good results. In this dissertation we compare recurrent neural networks with time-delayed feed forward networks, feed forward networks and linear regression models to see which architecture that can make the most accurate predictions. The data used in all experiments is real-world sales data containing two kinds of segment...
متن کاملNeural Networks for Chaotic Time Series Prediction
There are many systems that can be described as chaotic: The readings from seismic monitoring stations in mines which describe the rock dynamics, from EKG which describe the fibrillation of a cardiac patient’s heart, and the share prices in financial markets which describe the optimism about the earning potential of companies are examples of observations of deterministic, non−linear, dynamical ...
متن کاملOptimizing Neural Networks for Time Series Prediction
In this paper we investigate the eeective design of an appropriate neural network model for time series prediction based on an evolutionary approach. In particular, the Breeder Genetic Algorithms are considered to face contemporaneously the optimization of (i) the design of a neural network architecture and (ii) the choice of the best learning method. The eeectiveness of the approach proposed i...
متن کاملInferring gene regulatory networks from time series data using the minimum description length principle
MOTIVATION A central question in reverse engineering of genetic networks consists in determining the dependencies and regulating relationships among genes. This paper addresses the problem of inferring genetic regulatory networks from time-series gene-expression profiles. By adopting a probabilistic modeling framework compatible with the family of models represented by dynamic Bayesian networks...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Physical Review E
سال: 2002
ISSN: 1063-651X,1095-3787
DOI: 10.1103/physreve.66.066701