Permutation Complexity and Coupling Measures in Hidden Markov Models

In [Haruna, T. and Nakajima, K., 2011. Physica D 240, 1370-1377], the authors introduced the duality between values (words) and orderings (permutations) as a basis to discuss the relationship between information theoretic measures for finite-alphabet stationary stochastic processes and their permutation analogues. It has been used to give a simple proof of the equality between the entropy rate and the permutation entropy rate for any finite-alphabet stationary stochastic process and show some results on the excess entropy and the transfer entropy for finite-alphabet stationary ergodic Markov processes. In this paper, we extend our previous results to hidden Markov models and show the equalities between various information theoretic complexity and coupling measures and their permutation analogues. In particular, we show the following two results within the realm of hidden Markov models with ergodic internal processes: the two permutation analogues of the transfer entropy, the symbolic transfer entropy and the transfer entropy on rank vectors, are both equivalent to the transfer entropy if they are considered as the rates, and the directed information theory can be captured by the permutation entropy approach.Comment: 26 page

Non-Parametric Classification of Time Series Using Permutation Ordinal Statistics

The present thesis explores some approaches to classify time series without prior statistical information using the concept of permutation entropy. Motivated by the results from a previous published and relevant work that set similarity relationships between EEG time series, a reproduction of the proposed approach was performed giving negative results. The failure to reproduce those results led to the conclusion that the approach of building statistics from permutation patterns have to be complemented with another metric in order to be used for classification purposes. The concept of Total Variation Distance (TVD) was then used to develop three algorithms to classify time series in a non-parametric way. At first, the developed algorithms were tested using EEG time series. Even though the results using the developed algorithms were better than previous results, they were not as satisfactory as desired. However, the inherent complexity of brain measurements led to switch to self-generated data to test the algorithms. Using time series coming from different sets of filtered versions of Gaussian white noise the classification was performed. For comparison purposes a parametric classification approach using the Maximum Likelihood Estimation was also used. Results showed that when each set of data came from the same filtering equation the classification using the developed algorithms was optimal reaching 100% success rate in many cases, being as good as the ML approach. On the other hand, when each set of data came from a mixture of different filter equations that generate the time series (reflecting the complex situations we faced when processing EEG data) , results were fairly successful with variations with respect to the ML approach, which was outperformed in some cases but also not surpassed in others. The results obtained pointed the permutation entropy analysis to be an approach in the right direction to efficiently classify time series, however more research needs to be done to adjust the correct metric to get better results

In silico case studies of compliant robots: AMARSI deliverable 3.3

In the deliverable 3.2 we presented how the morphological computing ap- proach can significantly facilitate the control strategy in several scenarios, e.g. quadruped locomotion, bipedal locomotion and reaching. In particular, the Kitty experimental platform is an example of the use of morphological computation to allow quadruped locomotion. In this deliverable we continue with the simulation studies on the application of the different morphological computation strategies to control a robotic system