29 research outputs found
A statistical analysis of product prices in online markets
We empirically investigate fluctuations in product prices in online markets
by using a tick-by-tick price data collected from a Japanese price comparison
site, and find some similarities and differences between product and asset
prices. The average price of a product across e-retailers behaves almost like a
random walk, although the probability of price increase/decrease is higher
conditional on the multiple events of price increase/decrease. This is quite
similar to the property reported by previous studies about asset prices.
However, we fail to find a long memory property in the volatility of product
price changes. Also, we find that the price change distribution for product
prices is close to an exponential distribution, rather than a power law
distribution. These two findings are in a sharp contrast with the previous
results regarding asset prices. We propose an interpretation that these
differences may stem from the absence of speculative activities in product
markets; namely, e-retailers seldom repeat buy and sell of a product, unlike
traders in asset markets.Comment: 5 pages, 5 figures, 1 table, proceedings of APFA
Node-weighted measures for complex networks with spatially embedded, sampled, or differently sized nodes
When network and graph theory are used in the study of complex systems, a
typically finite set of nodes of the network under consideration is frequently
either explicitly or implicitly considered representative of a much larger
finite or infinite region or set of objects of interest. The selection
procedure, e.g., formation of a subset or some kind of discretization or
aggregation, typically results in individual nodes of the studied network
representing quite differently sized parts of the domain of interest. This
heterogeneity may induce substantial bias and artifacts in derived network
statistics. To avoid this bias, we propose an axiomatic scheme based on the
idea of node splitting invariance to derive consistently weighted variants of
various commonly used statistical network measures. The practical relevance and
applicability of our approach is demonstrated for a number of example networks
from different fields of research, and is shown to be of fundamental importance
in particular in the study of spatially embedded functional networks derived
from time series as studied in, e.g., neuroscience and climatology.Comment: 21 pages, 13 figure
Estimating the Fractal Dimension, K_2-entropy, and the Predictability of the Atmosphere
The series of mean daily temperature of air recorded over a period of 215
years is used for analysing the dimensionality and the predictability of the
atmospheric system. The total number of data points of the series is 78527.
Other 37 versions of the original series are generated, including ``seasonally
adjusted'' data, a smoothed series, series without annual course, etc. Modified
methods of Grassberger and Procaccia are applied. A procedure for selection of
the ``meaningful'' scaling region is proposed. Several scaling regions are
revealed in the ln C(r) versus ln r diagram. The first one in the range of
larger ln r has a gradual slope and the second one in the range of intermediate
ln r has a fast slope. Other two regions are settled in the range of small ln
r. The results lead us to claim that the series arises from the activity of at
least two subsystems. The first subsystem is low-dimensional (d_f=1.6) and it
possesses the potential predictability of several weeks. We suggest that this
subsystem is connected with seasonal variability of weather. The second
subsystem is high-dimensional (d_f>17) and its error-doubling time is about 4-7
days. It is found that the predictability differs in dependence on season. The
predictability time for summer, winter and the entire year (T_2 approx. 4.7
days) is longer than for transition-seasons (T_2 approx. 4.0 days for spring,
T_2 approx. 3.6 days for autumn). The role of random noise and the number of
data points are discussed. It is shown that a 15-year-long daily temperature
series is not sufficient for reliable estimations based on Grassberger and
Procaccia algorithms.Comment: 27 pages (LaTex version 2.09) and 15 figures as .ps files, e-mail:
[email protected]
Investigating the topology of interacting networks - Theory and application to coupled climate subnetworks
Network theory provides various tools for investigating the structural or
functional topology of many complex systems found in nature, technology and
society. Nevertheless, it has recently been realised that a considerable number
of systems of interest should be treated, more appropriately, as interacting
networks or networks of networks. Here we introduce a novel graph-theoretical
framework for studying the interaction structure between subnetworks embedded
within a complex network of networks. This framework allows us to quantify the
structural role of single vertices or whole subnetworks with respect to the
interaction of a pair of subnetworks on local, mesoscopic and global
topological scales.
Climate networks have recently been shown to be a powerful tool for the
analysis of climatological data. Applying the general framework for studying
interacting networks, we introduce coupled climate subnetworks to represent and
investigate the topology of statistical relationships between the fields of
distinct climatological variables. Using coupled climate subnetworks to
investigate the terrestrial atmosphere's three-dimensional geopotential height
field uncovers known as well as interesting novel features of the atmosphere's
vertical stratification and general circulation. Specifically, the new measure
"cross-betweenness" identifies regions which are particularly important for
mediating vertical wind field interactions. The promising results obtained by
following the coupled climate subnetwork approach present a first step towards
an improved understanding of the Earth system and its complex interacting
components from a network perspective
Quasi-chaotic behaviors of narrow-band response of a non-deterministic resonant system: application to analysis of ship motion in irregular seas
Challenges in network science: Applications to infrastructures, climate, social systems and economics
Exploiting geometric signatures to accurately determine properties of attractors
AbstractWe explore the notion of the geometric signature and demonstrate that it could be utilized in order to estimate dimensions, characterize lacunarity and type of attractor (self-similar, nonself-similar), and determine the length of transients for attractors
Variability and trends of landfalling atmospheric rivers along the Pacific Coast of northwestern North America
Inferring interdependencies in climate networks constructed at inter-annual, intra-season and longer time scales
We study global climate networks constructed by means of ordinal time series analysis. Climate interdependencies among the nodes are quantified by the mutual information, computed from time series of monthly-averaged surface air temperature anomalies, and from their symbolic ordinal representation (OP). This analysis allows identifying topological changes in the network when varying the time-interval of the ordinal pattern. We consider intra-season time-intervals (e.g., the patterns are formed by anomalies in consecutive months) and inter-annual time-intervals (e.g., the patterns are formed by anomalies in consecutive years). We discuss how the network density and topology change with these time scales, and provide evidence of correlations between geographically distant regions that occur at specific time scales. In particular, we find that an increase in the ordinal pattern spacing (i.e., an increase in the timescale of the ordinal analysis), results in climate networks with increased connectivity on the equatorial Pacific area. On the contrary, the number of significant links decreases when the ordinal analysis is done with a shorter timescale (by comparing consecutive months), and interpret this effect as due to more stochasticity in the time-series in the short timescale. As the equatorial Pacific is known to be dominated by El Niño-Southern Oscillation (ENSO) on scales longer than several months, our methodology allows constructing climate networks where the effect of ENSO goes from mild (monthly OP) to intense (yearly OP), independently of the length of the ordinal pattern and of the thresholding method employed. © 2013 EDP Sciences and Springer