512 research outputs found
Investment strategy due to the minimization of portfolio noise level by observations of coarse-grained entropy
Using a recently developed method of noise level estimation that makes use of
properties of the coarse grained-entropy we have analyzed the noise level for
the Dow Jones index and a few stocks from the New York Stock Exchange. We have
found that the noise level ranges from 40 to 80 percent of the signal variance.
The condition of a minimal noise level has been applied to construct optimal
portfolios from selected shares. We show that implementation of a corresponding
threshold investment strategy leads to positive returns for historical data.Comment: 6 pages, 1 figure, 1 table, Proceedings of the conference APFA4. See
http://www.chaosandnoise.or
Anti-deterministic behavior of discrete systems that are less predictable than noise
We present a new type of deterministic dynamical behaviour that is less
predictable than white noise. We call it anti-deterministic (AD) because time
series corresponding to the dynamics of such systems do not generate
deterministic lines in Recurrence Plots for small thresholds. We show that
although the dynamics is chaotic in the sense of exponential divergence of
nearby initial conditions and although some properties of AD data are similar
to white noise, the AD dynamics is in fact less predictable than noise and
hence is different from pseudo-random number generators.Comment: 6 pages, 5 figures. See http://www.chaosandnoise.or
Noise reduction in chaotic time series by a local projection with nonlinear constraints
On the basis of a local-projective (LP) approach we develop a method of noise
reduction in time series that makes use of nonlinear constraints appearing due
to the deterministic character of the underlying dynamical system. The Delaunay
triangulation approach is used to find the optimal nearest neighboring points
in time series. The efficiency of our method is comparable to standard LP
methods but our method is more robust to the input parameter estimation.
The approach has been successfully applied for separating a signal from noise
in the chaotic Henon and Lorenz models as well as for noisy experimental data
obtained from an electronic Chua circuit. The method works properly for a
mixture of additive and dynamical noise and can be used for the noise-level
detection.Comment: 11 pages, 12 figures. See http://www.chaosandnoise.or
Estimation of a Noise Level Using Coarse-Grained Entropy of Experimental Time Series of Internal Pressure in a Combustion Engine
We report our results on non-periodic experimental time series of pressure in
a single cylinder spark ignition engine. The experiments were performed for
different levels of loading. We estimate the noise level in internal pressure
calculating the coarse-grained entropy from variations of maximal pressures in
successive cycles. The results show that the dynamics of the combustion is a
nonlinear multidimensional process mediated by noise. Our results show that so
defined level of noise in internal pressure is not monotonous function of
loading.Comment: 12 pages, 6 figure
How random is your heart beat?
We measure the content of random uncorrelated noise in heart rate variability
using a general method of noise level estimation using a coarse grained
entropy. We show that usually - except for atrial fibrillation - the level of
such noise is within 5 - 15% of the variance of the data and that the
variability due to the linearly correlated processes is dominant in all cases
analysed but atrial fibrillation. The nonlinear deterministic content of heart
rate variability remains significant and may not be ignored.Comment: see http://urbanowicz.org.p
Risk evaluation with enhaced covariance matrix
We propose a route for the evaluation of risk based on a transformation of
the covariance matrix. The approach uses a `potential' or `objective' function.
This allows us to rescale data from different assets (or sources) such that
each data set then has similar statistical properties in terms of their
probability distributions. The method is tested using historical data from both
the New York and Warsaw Stock Exchanges.Comment: see urbanowicz.org.p
Automating biomedical data science through tree-based pipeline optimization
Over the past decade, data science and machine learning has grown from a
mysterious art form to a staple tool across a variety of fields in academia,
business, and government. In this paper, we introduce the concept of tree-based
pipeline optimization for automating one of the most tedious parts of machine
learning---pipeline design. We implement a Tree-based Pipeline Optimization
Tool (TPOT) and demonstrate its effectiveness on a series of simulated and
real-world genetic data sets. In particular, we show that TPOT can build
machine learning pipelines that achieve competitive classification accuracy and
discover novel pipeline operators---such as synthetic feature
constructors---that significantly improve classification accuracy on these data
sets. We also highlight the current challenges to pipeline optimization, such
as the tendency to produce pipelines that overfit the data, and suggest future
research paths to overcome these challenges. As such, this work represents an
early step toward fully automating machine learning pipeline design.Comment: 16 pages, 5 figures, to appear in EvoBIO 2016 proceeding
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