92,636 research outputs found
Expressing the entropy of lattice systems as sums of conditional entropies
Whether a system is to be considered complex or not depends on how one
searches for correlations. We propose a general scheme for calculation of
entropies in lattice systems that has high flexibility in how correlations are
successively taken into account. Compared to the traditional approach for
estimating the entropy density, in which successive approximations builds on
step-wise extensions of blocks of symbols, we show that one can take larger
steps when collecting the statistics necessary to calculate the entropy density
of the system. In one dimension this means that, instead of a single sweep over
the system in which states are read sequentially, one take several sweeps with
larger steps so that eventually the whole lattice is covered. This means that
the information in correlations is captured in a different way, and in some
situations this will lead to a considerably much faster convergence of the
entropy density estimate as a function of the size of the configurations used
in the estimate. The formalism is exemplified with both an example of a free
energy minimisation scheme for the two-dimensional Ising model, and an example
of increasingly complex spatial correlations generated by the time evolution of
elementary cellular automaton rule 60
Frequency Effects on Predictability of Stock Returns
We propose that predictability is a prerequisite for profitability on
financial markets. We look at ways to measure predictability of price changes
using information theoretic approach and employ them on all historical data
available for NYSE 100 stocks. This allows us to determine whether frequency of
sampling price changes affects the predictability of those. We also relations
between price changes predictability and the deviation of the price formation
processes from iid as well as the stock's sector. We also briefly comment on
the complicated relationship between predictability of price changes and the
profitability of algorithmic trading.Comment: 8 pages, 16 figures, submitted for possible publication to
Computational Intelligence for Financial Engineering and Economics 2014
conferenc
Time Resolution Dependence of Information Measures for Spiking Neurons: Atoms, Scaling, and Universality
The mutual information between stimulus and spike-train response is commonly
used to monitor neural coding efficiency, but neuronal computation broadly
conceived requires more refined and targeted information measures of
input-output joint processes. A first step towards that larger goal is to
develop information measures for individual output processes, including
information generation (entropy rate), stored information (statistical
complexity), predictable information (excess entropy), and active information
accumulation (bound information rate). We calculate these for spike trains
generated by a variety of noise-driven integrate-and-fire neurons as a function
of time resolution and for alternating renewal processes. We show that their
time-resolution dependence reveals coarse-grained structural properties of
interspike interval statistics; e.g., -entropy rates that diverge less
quickly than the firing rate indicate interspike interval correlations. We also
find evidence that the excess entropy and regularized statistical complexity of
different types of integrate-and-fire neurons are universal in the
continuous-time limit in the sense that they do not depend on mechanism
details. This suggests a surprising simplicity in the spike trains generated by
these model neurons. Interestingly, neurons with gamma-distributed ISIs and
neurons whose spike trains are alternating renewal processes do not fall into
the same universality class. These results lead to two conclusions. First, the
dependence of information measures on time resolution reveals mechanistic
details about spike train generation. Second, information measures can be used
as model selection tools for analyzing spike train processes.Comment: 20 pages, 6 figures;
http://csc.ucdavis.edu/~cmg/compmech/pubs/trdctim.ht
Outlier Detection Techniques For Wireless Sensor Networks: A Survey
In the field of wireless sensor networks, measurements that
significantly deviate from the normal pattern of sensed data are
considered as outliers. The potential sources of outliers include
noise and errors, events, and malicious attacks on the network.
Traditional outlier detection techniques are not directly
applicable to wireless sensor networks due to the multivariate
nature of sensor data and specific requirements and limitations of
the wireless sensor networks. This survey provides a comprehensive
overview of existing outlier detection techniques specifically
developed for the wireless sensor networks. Additionally, it
presents a technique-based taxonomy and a decision tree to be used
as a guideline to select a technique suitable for the application
at hand based on characteristics such as data type, outlier type,
outlier degree
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