800,650 research outputs found
Detecting structural breaks in seasonal time series by regularized optimization
Real-world systems are often complex, dynamic, and nonlinear. Understanding
the dynamics of a system from its observed time series is key to the prediction
and control of the system's behavior. While most existing techniques tacitly
assume some form of stationarity or continuity, abrupt changes, which are often
due to external disturbances or sudden changes in the intrinsic dynamics, are
common in time series. Structural breaks, which are time points at which the
statistical patterns of a time series change, pose considerable challenges to
data analysis. Without identification of such break points, the same dynamic
rule would be applied to the whole period of observation, whereas false
identification of structural breaks may lead to overfitting. In this paper, we
cast the problem of decomposing a time series into its trend and seasonal
components as an optimization problem. This problem is ill-posed due to the
arbitrariness in the number of parameters. To overcome this difficulty, we
propose the addition of a penalty function (i.e., a regularization term) that
accounts for the number of parameters. Our approach simultaneously identifies
seasonality and trend without the need of iterations, and allows the reliable
detection of structural breaks. The method is applied to recorded data on fish
populations and sea surface temperature, where it detects structural breaks
that would have been neglected otherwise. This suggests that our method can
lead to a general approach for the monitoring, prediction, and prevention of
structural changes in real systems.Comment: Safety, Reliability, Risk and Life-Cycle Performance of Structures
and Infrastructures (Edited by George Deodatis, Bruce R. Ellingwood and Dan
M. Frangopol), CRC Press 2014, Pages 3621-362
Animating the development of Social Networks over time using a dynamic extension of multidimensional scaling
The animation of network visualizations poses technical and theoretical
challenges. Rather stable patterns are required before the mental map enables a
user to make inferences over time. In order to enhance stability, we developed
an extension of stress-minimization with developments over time. This dynamic
layouter is no longer based on linear interpolation between independent static
visualizations, but change over time is used as a parameter in the
optimization. Because of our focus on structural change versus stability the
attention is shifted from the relational graph to the latent eigenvectors of
matrices. The approach is illustrated with animations for the journal citation
environments of Social Networks, the (co-)author networks in the carrying
community of this journal, and the topical development using relations among
its title words. Our results are also compared with animations based on
PajekToSVGAnim and SoNIA
Wavelet-Based Prediction for Governance, Diversification and Value Creation Variables
We study the possibility of completing data bases of a sample of governance,
diversification and value creation variables by providing a well adapted method
to reconstruct the missing parts in order to obtain a complete sample to be
applied for testing the ownership-structure/diversification relationship. It
consists of a dynamic procedure based on wavelets. A comparison with Neural
Networks, the most used method, is provided to prove the efficiency of the
here-developed one. The empirical tests are conducted on a set of French firms.Comment: 22 page
Nonâhierarchical learning: sharing knowledge, power and outcomes
Arguing that every student has the capacity to succeed and that every student must be provided with the opportunity to reach their full potential, this article introduces a new pedagogic approach that draws on a wide range of influences. Linking theoretical practices from sociology, pedagogy, social and educational psychology, and cultural studies, the approach posits that teaching and learning should be conducted in nonâhierarchical classrooms where all members are equal and working towards shared objectives. A theoretical frame is outlined and the factors that helped shape it are reflected on. A conceptual framework which covers the goals of instruction, instructional materials, classroom management, instructional methods, and assessment is also presented. It is hoped that educators will consider the concepts included in this article and, if possible, incorporate them into their teaching practices
Structure and Dynamics of Information Pathways in Online Media
Diffusion of information, spread of rumors and infectious diseases are all
instances of stochastic processes that occur over the edges of an underlying
network. Many times networks over which contagions spread are unobserved, and
such networks are often dynamic and change over time. In this paper, we
investigate the problem of inferring dynamic networks based on information
diffusion data. We assume there is an unobserved dynamic network that changes
over time, while we observe the results of a dynamic process spreading over the
edges of the network. The task then is to infer the edges and the dynamics of
the underlying network.
We develop an on-line algorithm that relies on stochastic convex optimization
to efficiently solve the dynamic network inference problem. We apply our
algorithm to information diffusion among 3.3 million mainstream media and blog
sites and experiment with more than 179 million different pieces of information
spreading over the network in a one year period. We study the evolution of
information pathways in the online media space and find interesting insights.
Information pathways for general recurrent topics are more stable across time
than for on-going news events. Clusters of news media sites and blogs often
emerge and vanish in matter of days for on-going news events. Major social
movements and events involving civil population, such as the Libyan's civil war
or Syria's uprise, lead to an increased amount of information pathways among
blogs as well as in the overall increase in the network centrality of blogs and
social media sites.Comment: To Appear at the 6th International Conference on Web Search and Data
Mining (WSDM '13
The Child is Father of the Man: Foresee the Success at the Early Stage
Understanding the dynamic mechanisms that drive the high-impact scientific
work (e.g., research papers, patents) is a long-debated research topic and has
many important implications, ranging from personal career development and
recruitment search, to the jurisdiction of research resources. Recent advances
in characterizing and modeling scientific success have made it possible to
forecast the long-term impact of scientific work, where data mining techniques,
supervised learning in particular, play an essential role. Despite much
progress, several key algorithmic challenges in relation to predicting
long-term scientific impact have largely remained open. In this paper, we
propose a joint predictive model to forecast the long-term scientific impact at
the early stage, which simultaneously addresses a number of these open
challenges, including the scholarly feature design, the non-linearity, the
domain-heterogeneity and dynamics. In particular, we formulate it as a
regularized optimization problem and propose effective and scalable algorithms
to solve it. We perform extensive empirical evaluations on large, real
scholarly data sets to validate the effectiveness and the efficiency of our
method.Comment: Correct some typos in our KDD pape
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