Evolvable Neural Networks for Time Series Prediction with Adaptive Learning Interval

Evolvable Neural Networks for Time Series Prediction with Adaptive Learning Interval

Time Series Prediction with Artiicial Neural Networks

This research project investigates the ability of artiicial neural networks (ANNs) to predict time series. Speciically, this project examined two applications: predicting electrical power demand and earthquake tremors in Los Angeles. The ANNs modeling power demand not only predicted the next day's peak power demand, but also generated a 24-hour proole of the demand for the next day. These ANNs ...

Recurrent 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...

Neural 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 ...

Optimizing 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...