Hausdorff clustering of financial time series

A clustering procedure, based on the Hausdorff distance, is introduced and tested on the financial time series of the Dow Jones Industrial Average (DJIA) index.Comment: 9 pages, 3 figure

Autism research : An objective quantitative review of progress and focus between 1994 and 2015

The nosology and epidemiology of Autism has undergone transformation following consolidation of once disparate disorders under the umbrella diagnostic, autism spectrum disorders. Despite this re-conceptualization, research initiatives, including the NIMH's Research Domain Criteria and Precision Medicine, highlight the need to bridge psychiatric and psychological classification methodologies with biomedical techniques. Combining traditional bibliometric co-word techniques, with tenets of graph theory and network analysis, this article provides an objective thematic review of research between 1994 and 2015 to consider evolution and focus. Results illustrate growth in Autism research since 2006, with nascent focus on physiology. However, modularity and citation analytics demonstrate dominance of subjective psychological or psychiatric constructs, which may impede progress in the identification and stratification of biomarkers as endorsed by new research initiatives.Peer reviewedFinal Published versio

Adaptive Evolutionary Clustering

In many practical applications of clustering, the objects to be clustered evolve over time, and a clustering result is desired at each time step. In such applications, evolutionary clustering typically outperforms traditional static clustering by producing clustering results that reflect long-term trends while being robust to short-term variations. Several evolutionary clustering algorithms have recently been proposed, often by adding a temporal smoothness penalty to the cost function of a static clustering method. In this paper, we introduce a different approach to evolutionary clustering by accurately tracking the time-varying proximities between objects followed by static clustering. We present an evolutionary clustering framework that adaptively estimates the optimal smoothing parameter using shrinkage estimation, a statistical approach that improves a naive estimate using additional information. The proposed framework can be used to extend a variety of static clustering algorithms, including hierarchical, k-means, and spectral clustering, into evolutionary clustering algorithms. Experiments on synthetic and real data sets indicate that the proposed framework outperforms static clustering and existing evolutionary clustering algorithms in many scenarios.Comment: To appear in Data Mining and Knowledge Discovery, MATLAB toolbox available at http://tbayes.eecs.umich.edu/xukevin/affec

Self-Organizing Time Map: An Abstraction of Temporal Multivariate Patterns

This paper adopts and adapts Kohonen's standard Self-Organizing Map (SOM) for exploratory temporal structure analysis. The Self-Organizing Time Map (SOTM) implements SOM-type learning to one-dimensional arrays for individual time units, preserves the orientation with short-term memory and arranges the arrays in an ascending order of time. The two-dimensional representation of the SOTM attempts thus twofold topology preservation, where the horizontal direction preserves time topology and the vertical direction data topology. This enables discovering the occurrence and exploring the properties of temporal structural changes in data. For representing qualities and properties of SOTMs, we adapt measures and visualizations from the standard SOM paradigm, as well as introduce a measure of temporal structural changes. The functioning of the SOTM, and its visualizations and quality and property measures, are illustrated on artificial toy data. The usefulness of the SOTM in a real-world setting is shown on poverty, welfare and development indicators

Tracking Cluster Debris (TraCD) – I. Dissolution of clusters and searching for the solar cradle

The capability to reconstruct dissolved stellar systems in dynamical and chemical space is a key factor in improving our understanding of the evolution of the Milky Way. Here we concentrate on the dynamical aspect and given that a significant portion of the stars in the Milky Way have been born in stellar associations or clusters that have lived a few Myr up to several Gyr, we further restrict our attention to the evolution of star clusters. We have carried out our simulations in two steps: (1) we create a simulation of dissolution and mixing processes which yields a close fit to the present-day Milky Way dynamics and (2) we have evolved three sets of stellar clusters with masses of 400, 1000 and 15 000 M⊙ to dissolution. The birth location of these sets was 4, 6, 8 and 10 kpc for the 400 and 1000 M⊙ clusters and 4, 6, 8, 10 and 12 kpc for the 15 000 M⊙. We have focused our efforts on studying the state of the escapers from these clusters after 4.5 Gyr of evolution with particular attention to stars that reach the solar annulus, i.e. 7.5 ≤ Rgc ≤ 8.5 kpc. We give results for solar twins and siblings over a wide range of radii and cluster masses for two dissolution mechanisms. From kinematics alone, we conclude that the Sun was ∼50 per cent more likely to have been born near its current Galactocentric radius, rather than have migrated (radially) ∼2 kpc since birth. We conclude our analysis by calculating magnitudes and colours of our single stars for comparison with the samples that the Gaia, Gaia-ESO and GALAH-AAO surveys will obtain. In terms of reconstructing dissolved star clusters, we find that on short time-scales we cannot rely on kinematic evolution alone and thus it will be necessary to extend our study to include information on chemical space

Libor at crossroads: stochastic switching detection using information theory quantifiers

This paper studies the 28 time series of Libor rates, classified in seven maturities and four currencies), during the last 14 years. The analysis was performed using a novel technique in financial economics: the Complexity-Entropy Causality Plane. This planar representation allows the discrimination of different stochastic and chaotic regimes. Using a temporal analysis based on moving windows, this paper unveals an abnormal movement of Libor time series arround the period of the 2007 financial crisis. This alteration in the stochastic dynamics of Libor is contemporary of what press called "Libor scandal", i.e. the manipulation of interest rates carried out by several prime banks. We argue that our methodology is suitable as a market watch mechanism, as it makes visible the temporal redution in informational efficiency of the market.Comment: 17 pages, 9 figures. arXiv admin note: text overlap with arXiv:1508.04748, arXiv:1509.0021

Dynamics of episodic transient correlations in currency exchange rate returns and their predictability

We study the dynamics of the linear and non-linear serial dependencies in financial time series in a rolling window framework. In particular, we focus on the detection of episodes of statistically significant two- and three-point correlations in the returns of several leading currency exchange rates that could offer some potential for their predictability. We employ a rolling window approach in order to capture the correlation dynamics for different window lengths and analyze the distributions of periods with statistically significant correlations. We find that for sufficiently large window lengths these distributions fit well to power-law behavior. We also measure the predictability itself by a hit rate, i.e. the rate of consistency between the signs of the actual returns and their predictions, obtained from a simple correlation-based predictor. It is found that during these relatively brief periods the returns are predictable to a certain degree and the predictability depends on the selection of the window length.Comment: 19 pages, 8 figure