Probabilistic Finite-State Machines-Part II
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Finite-State Machines-Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hidden Markov models with states depending on observations
Pattern Recognition Letters
A Hidden Markov Model Approach to Classify and Predict the Sign of Financial Local Trends
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Component-based discriminative classification for hidden Markov models
Pattern Recognition
Neuro-genetic system for stock index prediction
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Evolutionary neural networks for practical applications
An input-output hidden Markov model for tree transductions
Neurocomputing
Hi-index | 0.00 |
Input-output hidden Markov models (IOHMM) are conditional hidden Markov models in which the emission (and possibly the transition) probabilities can be conditioned on an input sequence. For example, these conditional distributions can be linear, logistic, or nonlinear (using for example multilayer neural networks). We compare the generalization performance of several models which are special cases of input-output hidden Markov models on financial time-series prediction tasks: an unconditional Gaussian, a conditional linear Gaussian, a mixture of Gaussians, a mixture of conditional linear Gaussians, a hidden Markov model, and various IOHMMs. The experiments compare these models on predicting the conditional density of returns of market and sector indices. Note that the unconditional Gaussian estimates the first moment with the historical average. The results show that, although for the first moment the historical average gives the best results, for the higher moments, the IOHMMs yielded significantly better performance, as estimated by the out-of-sample likelihood