Prediction of financial time series with Time-Line Hidden Markov Experts and ANNs
Part of : WSEAS transactions on business and economics ; Vol.4, No.9, 2007, pages 140-145
Issue:
Pages:
140-145
Author:
Abstract:
In this article, the use of Time-Line Hidden Markov Experts (THME) in the prediction of financial time series is presented and its efficiency is compared with that obtained using multilayer perceptron neural networks trained with BKP. The THME belongs to a focus known as mixture of experts, whose philosophy consists in decomposing the times series in states. Each expert models a particular state to achieve capturing the time series patterns in a sufficiently precise way, since for every situation in which time series can be found there is one or more experts that have the capacity to generate an adequate prognosis for the given situation. The state transition of each time series is time-variant. Experiments were carried out with 15 series of financial time series in which most of the world’s bursatile indexes can be found. The results show that THME models greatly surpass those of Artificial Neural Networks.
Subject (LC):
Keywords:
THME, HMM, ANN, financial time series, mixture of experts, fuzzy clustering
Notes:
Περιέχει γραφήματα, πίνακες και βιβλιογραφία
