19,692 research outputs found
Time series analysis for minority game simulations of financial markets
The minority game (MG) model introduced recently provides promising insights
into the understanding of the evolution of prices, indices and rates in the
financial markets. In this paper we perform a time series analysis of the model
employing tools from statistics, dynamical systems theory and stochastic
processes. Using benchmark systems and a financial index for comparison,
several conclusions are obtained about the generating mechanism for this kind
of evolut ion. The motion is deterministic, driven by occasional random
external perturbation. When the interval between two successive perturbations
is sufficiently large, one can find low dimensional chaos in this regime.
However, the full motion of the MG model is found to be similar to that of the
first differences of the SP500 index: stochastic, nonlinear and (unit root)
stationary.Comment: LaTeX 2e (elsart), 17 pages, 3 EPS figures and 2 tables, accepted for
publication in Physica
Compression and diffusion: a joint approach to detect complexity
The adoption of the Kolmogorov-Sinai (KS) entropy is becoming a popular
research tool among physicists, especially when applied to a dynamical system
fitting the conditions of validity of the Pesin theorem. The study of time
series that are a manifestation of system dynamics whose rules are either
unknown or too complex for a mathematical treatment, is still a challenge since
the KS entropy is not computable, in general, in that case. Here we present a
plan of action based on the joint action of two procedures, both related to the
KS entropy, but compatible with computer implementation through fast and
efficient programs. The former procedure, called Compression Algorithm
Sensitive To Regularity (CASToRe), establishes the amount of order by the
numerical evaluation of algorithmic compressibility. The latter, called Complex
Analysis of Sequences via Scaling AND Randomness Assessment (CASSANDRA),
establishes the complexity degree through the numerical evaluation of the
strength of an anomalous effect. This is the departure, of the diffusion
process generated by the observed fluctuations, from ordinary Brownian motion.
The CASSANDRA algorithm shares with CASToRe a connection with the Kolmogorov
complexity. This makes both algorithms especially suitable to study the
transition from dynamics to thermodynamics, and the case of non-stationary time
series as well. The benefit of the joint action of these two methods is proven
by the analysis of artificial sequences with the same main properties as the
real time series to which the joint use of these two methods will be applied in
future research work.Comment: 27 pages, 9 figure
Deconstructing Javanese Batik Motif: When Traditional Heritage Meets Computation
The paper discusses some aspects of Iterated Function System while referring to some interesting point of view into Indonesian traditional batik. The deconstruction is delivered in our recognition of the Collage Theorem to find the affine transform of the iterated function system that attracts the iteration of drawing the dots into the complex motif of – or at least, having high similarity to – batik patterns. We employ and revisit the well-known Chaos Game to reconstruct after having some basic motifs is deconstructed. The reconstruction of the complex pattern opens a quest of creativity broadening the computationally generated batik exploiting its self-similarity properties. A challenge to meet the modern computational generative art with the traditional batik designs is expected to yield synergistically interesting results aesthetically. The paper concludes with two arrows of our further endeavors in this field, be it enriching our understanding of how human cognition has created such beautiful patterns and designs traditionally since ancient civilizations in our anthropological perspective while in the other hand providing us tool to the empowerment of batik as generative aesthetics by employment of computation. \u
A Tutorial on Bayesian Nonparametric Models
A key problem in statistical modeling is model selection, how to choose a
model at an appropriate level of complexity. This problem appears in many
settings, most prominently in choosing the number ofclusters in mixture models
or the number of factors in factor analysis. In this tutorial we describe
Bayesian nonparametric methods, a class of methods that side-steps this issue
by allowing the data to determine the complexity of the model. This tutorial is
a high-level introduction to Bayesian nonparametric methods and contains
several examples of their application.Comment: 28 pages, 8 figure
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